nach oben

Erschienen in:

2022 | OriginalPaper | Buchkapitel

Selected Aspects of Interactive Feature Extraction

verfasst von : Marek Grzegorowski

Erschienen in: Transactions on Rough Sets XXIII

Verlag: Springer Berlin Heidelberg

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

In the presented study, the problem of interactive feature extraction, i.e., supported by interaction with users, is discussed, and several innovative approaches to automating feature creation and selection are proposed. The current state of knowledge on feature extraction processes in commercial applications is shown. The problems associated with processing big data sets as well as approaches to process high-dimensional time series are discussed. The introduced feature extraction methods were subjected to experimental verification on real-life problems and data. Besides the experimentation, the practical case studies and applications of developed techniques in selected scientific projects are shown.

Feature extraction addresses the problem of finding the most compact and informative data representation resulting in improved efficiency of data storage and processing, facilitating the subsequent learning and generalization steps. Feature extraction not only simplifies the data representation but also enables the acquisition of features that can be further easily utilized by both analysts and learning algorithms. In its most common flow, the process starts from an initial set of measured data and builds derived features intended to be informative and non-redundant. Logically, there are two phases of this process: the first is the construction of the new attributes based on original data (sometimes referred to as feature engineering), the second is a selection of the most important among the attributes (sometimes referred to as feature selection). There are many approaches to feature creation and selection that are well-described in the literature. Still, it is hard to find methods facilitating interaction with users, which would take into consideration users’ knowledge about the domain, their experience, and preferences.

In the study on the interactiveness of the feature extraction, the problems of deriving useful and understandable attributes from raw sensor readings and reducing the amount of those attributes to achieve possibly simplest, yet accurate, models are addressed. The proposed methods go beyond the current standards by enabling a more efficient way to express the domain knowledge associated with the most important subsets of attributes. The proposed algorithms for the construction and selection of features can use various forms of information granulation, problem decomposition, and parallelization. They can also tackle large spaces of derivable features and ensure a satisfactory (according to a given criterion) level of information about the target variable (decision), even after removing a substantial number of features.

The proposed approaches have been developed based on the experience gained in the course of several research projects in the fields of data analysis and processing multi-sensor data streams. The methods have been validated in terms of the quality of the extracted features, as well as throughput, scalability, and robustness of their operation. The discussed methodology has been verified in open data mining competitions to confirm its usefulness.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Vorheriges Kapitel The RSDS - Bibliographic Database for Rough Sets and Related Fields

Nächstes Kapitel A Study of Algebraic Structures and Logics Based on Categories of Rough Sets

Nur mit Berechtigung zugänglich

Later in this section, we discuss real matrices, as they are more relevant for real-life data sets.

We call a matrix \(X \in \mathbb {Z}^{n \times n}\) unitary iff \(X X^{H} = X^{H} X = \mathbb {I}\). For a real matrix \(X \in \mathbb {R}^{n \times n}\), we have \(X^{H} = X^{T}\), and we say that a matrix is orthogonal, i.e., \(X X^{T} = X^{T} X = \mathbb {I}\).

\(Cov[X,Y] = E[XY] - E[X]E[Y]\), or \(Cov[X,Y] = \frac{1}{m} \sum _{1 \le i \le m} (x_i - E[X])(y_i - E[Y])\).

In literature, matrix \(\textbf{V}\) is often denoted as \(\textbf{W}\), whereas \(\mathbf {\Sigma }\) as \(\mathbf {\Lambda }\). We, however, continue with the notation as introduces with SVD example above.

For a given corpus, the co-occurrence of two words is the number of times they appear together (and are close enough, e.g., no more than 30 words separates them in text) in documents.

The main topic for documents \(\{D_1, D_2, D_3\}\) could be related to “feature selection”.

It is worth mentioning that the neural network input is a numeric vector embedding for each word (typically, word vectorization is performed after the initial preprocessing).

Entropy is one of the basic measures of information contained in data. For a discrete random variable X with possible values \(\{x_1, .., x_m\}\) is defined as: \(H(X) = -\sum _{i=1}^{m} p(x_i)log(p(x_i))\).

Typically the set of all features/attributes is denoted with A [291].

If attribute domains are overlapping, i.e., there exist \(a_i,a_j \in A\) for which \(V_{a_i} \cap V_{a_j} \ne \emptyset \), then concatenation may include a delimiter \(\mid _A\) such that for each \(a \in A\) we have \(\mid _A \notin V_a\).

http://hadoop.apache.org.

https://docs.microsoft.com/en-us/azure/storage/storage-redundancy.

The description for the classification tasks would differ mainly in the training algorithms, and model evaluation criteria used.

AAIA’15, https://fedcsis.org/aaia.

aws.amazon.com/ec2/spot/.

azure.microsoft.com/en-us/pricing/spot/.

See AWS global cloud infrastructure at aws.amazon.com/about-aws/global-infrastructure.

AWS code-names for regions in brackets.

See docs.aws.amazon.com/emr/latest/ManagementGuide/emr-supported-instance-types.

The video recording is available on-line on KnowlegePit platform.

Numbers in brackets indicate the amount of unique bids in data for each region.

Abdel-Basset, M., El-Shahat, D., El-Henawy, I.M., de Albuquerque, V.H.C., Mirjalili, S.: A new fusion of grey wolf optimizer algorithm with a two-phase mutation for feature selection. Expert Syst. Appl. 139, 112824 (2020). https://doi.org/10.1016/j.eswa.2019.112824CrossRef

Abedjan, Z., et al.: Data science in healthcare: benefits, challenges and opportunities. In: Consoli, S., Recupero, D.R., Petkovic, M. (eds.) Data Science for Healthcare, pp. 3–38. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-05249-2_1CrossRef

Abeel, T., Helleputte, T., de Peer, Y.V., Dupont, P., Saeys, Y.: Robust biomarker identification for cancer diagnosis with ensemble feature selection methods. Bioinformatics 26(3), 392–398 (2010)CrossRef

Adebiyi, A.A., Adewumi, A.O., Ayo, C.K.: Comparison of ARIMA and artificial neural networks models for stock price prediction. J. Appl. Math. 2014, 614342:1–614342:7 (2014). https://doi.org/10.1155/2014/614342

U. S. Fire Administration: Annual report on firefighter fatalities in the United States. http://apps.usfa.fema.gov/firefighter-fatalities/

Aggarwal, C. (ed.): Managing and Mining Sensor Data. Springer, New York (2013). https://doi.org/10.1007/978-1-4614-6309-2CrossRef

Agrawal, A., et al.: Approximate computing: challenges and opportunities. In: IEEE International Conference on Rebooting Computing, ICRC 2016, San Diego, CA, USA, 17–19, October 2016, pp. 1–8. IEEE Computer Society (2016). https://doi.org/10.1109/ICRC.2016.7738674

Ahmadi, E., Jasemi, M., Monplaisir, L., Nabavi, M.A., Mahmoodi, A., Jam, P.A.: New efficient hybrid candlestick technical analysis model for stock market timing on the basis of the support vector machine and heuristic algorithms of imperialist competition and genetic. Expert Syst. Appl. 94, 21–31 (2018). https://doi.org/10.1016/j.eswa.2017.10.023CrossRef

Ahmed, F., Samorani, M., Bellinger, C., Zaïane, O.R.: Advantage of integration in big data: feature generation in multi-relational databases for imbalanced learning. In: Proceedings of IEEE Big Data, pp. 532–539 (2016)

10.

Aksoy, S., Haralick, R.M.: Feature normalization and likelihood-based similarity measures for image retrieval. Pattern Recogn. Lett. 22(5), 563–582 (2001). https://doi.org/10.1016/S0167-8655(00)00112-4CrossRefMATH

11.

Al-Ali, H., Cuzzocrea, A., Damiani, E., Mizouni, R., Tello, G.: A composite machine-learning-based framework for supporting low-level event logs to high-level business process model activities mappings enhanced by flexible BPMN model translation. Soft. Comput. 24(10), 7557–7578 (2019). https://doi.org/10.1007/s00500-019-04385-6CrossRef

12.

Alelyani, S., Tang, J., Liu, H.: Feature selection for clustering: a review. In: Aggarwal, C.C., Reddy, C.K. (eds.) Data Clustering: Algorithms and Applications, pp. 29–60. CRC Press, Boca Raton (2013)

13.

Almeida, F., Xexéo, G.: Word embeddings: a survey. CoRR abs/1901.09069 (2019). http://arxiv.org/abs/1901.09069

14.

Altidor, W., Khoshgoftaar, T.M., Napolitano, A.: Measuring stability of feature ranking techniques: a noise-based approach. Int. J. Bus. Intell. Data Min. 7(1–2), 80–115 (2012)

15.

Appice, A., Guccione, P., Malerba, D., Ciampi, A.: Dealing with temporal and spatial correlations to classify outliers in geophysical data streams. Inf. Sci. 285, 162–180 (2014)MathSciNetMATHCrossRef

16.

Assunção, M.D., Calheiros, R.N., Bianchi, S., Netto, M.A., Buyya, R.: Big data computing and clouds: trends and future directions. J. Parallel Distrib. Comput. 79, 3–15 (2015)CrossRef

17.

Augustyniak, P., Smoleń, M., Mikrut, Z., Kańtoch, E.: Seamless tracing of human behavior using complementary wearable and house-embedded sensors. Sensors 14(5), 7831–7856 (2014). https://doi.org/10.3390/s140507831CrossRef

18.

Ayesha, S., Hanif, M.K., Talib, R.: Overview and comparative study of dimensionality reduction techniques for high dimensional data. Inf. Fusion 59, 44–58 (2020). https://doi.org/10.1016/j.inffus.2020.01.005CrossRef

19.

Azad, M., Moshkov, M.: Minimization of decision tree average depth for decision tables with many-valued decisions. Procedia Comput. Sci. 35, 368–377 (2014). https://doi.org/10.1016/j.procs.2014.08.117. Knowledge-Based and Intelligent Information & Engineering Systems 18th Annual Conference, KES-2014 Gdynia, Poland, September 2014 Proceedings

20.

Bahmani, B., Moseley, B., Vattani, A., Kumar, R., Vassilvitskii, S.: Scalable K-Means++. Proc. VLDB Endow. 5(7), 622–633 (2012)CrossRef

21.

Bałazińska, M., Zdonik, S.: Databases meet the stream processing era, pp. 225–234. Association for Computing Machinery and Morgan and Claypool (2018)

22.

Bansal, A., Jain, R., Modi, K.: Big data streaming with spark. In: Mittal, M., Balas, V.E., Goyal, L.M., Kumar, R. (eds.) Big Data Processing Using Spark in Cloud. SBD, vol. 43, pp. 23–50. Springer, Singapore (2019). https://doi.org/10.1007/978-981-13-0550-4_2CrossRef

23.

Bargiela, A., Pedrycz, W.: The roots of granular computing. In: 2006 IEEE International Conference on Granular Computing, pp. 806–809. IEEE (2006)

24.

de Barros, R.S.M., de Carvalho Santos, S.G.T.: An overview and comprehensive comparison of ensembles for concept drift. Inf. Fusion 52, 213–244 (2019). https://doi.org/10.1016/j.inffus.2019.03.006CrossRef

25.

Bashir, F., Wei, H.L.: Handling missing data in multivariate time series using a vector autoregressive model-imputation (VAR-IM) algorithm. Neurocomputing 276, 23–30 (2018). https://doi.org/10.1016/j.neucom.2017.03.097. Machine Learning and Data Mining Techniques for Medical Complex Data Analysis

26.

Baughman, M., Haas, C., Wolski, R., Foster, I., Chard, K.: Predicting amazon spot prices with LSTM networks. In: Proceedings of the 9th Workshop on Scientific Cloud Computing, ScienceCloud 2018, p. 7. Association for Computing Machinery, New York (2018). https://doi.org/10.1145/3217880.3217881

27.

Bazan, J.G.: Hierarchical classifiers for complex Spatio-temporal concepts. In: Peters, J.F., Skowron, A., Rybiński, H. (eds.) Transactions on Rough Sets IX. LNCS, vol. 5390, pp. 474–750. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-89876-4_26CrossRef

28.

Bazan, J.G., Bazan-Socha, S., Buregwa-Czuma, S., Dydo, Ł, Rząsa, W., Skowron, A.: A classifier based on a decision tree with verifying cuts. Fundam. Informaticae 143(1–2), 1–18 (2016). https://doi.org/10.3233/FI-2016-1300MathSciNetCrossRef

29.

Bengio, Y., Courville, A.C., Vincent, P.: Representation learning: a review and new perspectives. IEEE Trans. Pattern Anal. Mach. Intell. 35(8), 1798–1828 (2013). https://doi.org/10.1109/TPAMI.2013.50CrossRef

30.

Benítez-Caballero, M.J., Medina, J., Ramírez-Poussa, E., Ślęzak, D.: A computational procedure for variable selection preserving different initial conditions. Int. J. Comput. Math. 97(1–2), 387–404 (2020). https://doi.org/10.1080/00207160.2019.1613530MathSciNetCrossRefMATH

31.

Benítez-Peña, S., Blanquero, R., Carrizosa, E., Ramírez-Cobo, P.: Cost-sensitive feature selection for support vector machines. Comput. Oper. Res. 106, 169–178 (2019). https://doi.org/10.1016/j.cor.2018.03.005MathSciNetCrossRefMATH

32.

Bennasar, M., Hicks, Y., Setchi, R.: Feature selection using joint mutual information maximisation. Expert Syst. Appl. 42(22), 8520–8532 (2015). https://doi.org/10.1016/j.eswa.2015.07.007CrossRef

33.

Benoit, F., van Heeswijk, M., Miche, Y., Verleysen, M., Lendasse, A.: Feature selection for nonlinear models with extreme learning machines. Neurocomputing 102, 111–124 (2013). https://doi.org/10.1016/j.neucom.2011.12.055CrossRef

34.

Berrado, A., Runger, G.C.: Supervised multivariate discretization in mixed data with random forests. In: 2009 IEEE/ACS International Conference on Computer Systems and Applications, pp. 211–217, May 2009. https://doi.org/10.1109/AICCSA.2009.5069327

35.

Berrouachedi, A., Jaziri, R., Bernard, G.: Deep extremely randomized trees. In: Gedeon, T., Wong, K.W., Lee, M. (eds.) ICONIP 2019. LNCS, vol. 11953, pp. 717–729. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-36708-4_59CrossRef

36.

Biau, G., Cadre, B., Rouvière, L.: Accelerated gradient boosting. Mach. Learn. 108(6), 971–992 (2019). https://doi.org/10.1007/s10994-019-05787-1MathSciNetCrossRefMATH

37.

Bifet, A., Holmes, G., Pfahringer, B., Kirkby, R., Gavaldà, R.: New ensemble methods for evolving data streams. In: IV, J.F.E., Fogelman-Soulié, F., Flach, P.A., Zaki, M.J. (eds.) Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Paris, France, 28 June–1 July 2009, pp. 139–148. ACM (2009). https://doi.org/10.1145/1557019.1557041

38.

Bolón-Canedo, V., Alonso-Betanzos, A.: Ensembles for feature selection: a review and future trends. Inf. Fusion 52, 1–12 (2019). https://doi.org/10.1016/j.inffus.2018.11.008CrossRef

39.

Bolstad, B.M., Irizarry, R.A., Åstrand, M., Speed, T.P.: A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics 19(2), 185–193 (2003). https://doi.org/10.1093/bioinformatics/19.2.185CrossRef

40.

Bommert, A., Sun, X., Bischl, B., Rahnenführer, J., Lang, M.: Benchmark for filter methods for feature selection in high-dimensional classification data. Comput. Stat. Data Anal. 143, 106839 (2020). https://doi.org/10.1016/j.csda.2019.106839MathSciNetCrossRefMATH

41.

Bondell, H.D., Reich, B.J.: Simultaneous regression shrinkage, variable selection, and supervised clustering of predictors with OSCAR. Biometrics 64(1), 115–123 (2008)MathSciNetMATHCrossRef

42.

Borchani, H., Varando, G., Bielza, C., Larrañaga, P.: A survey on multi-output regression. Wiley Int. Rev. Data Min. and Knowl. Disc. 5, 216–233 (2015). https://doi.org/10.1002/widm.1157CrossRef

43.

Boullé, M.: MODL: a Bayes optimal discretization method for continuous attributes. Mach. Learn. 65(1), 131–165 (2006). https://doi.org/10.1007/s10994-006-8364-xCrossRefMATH

44.

Boullé, M.: Prediction of methane outbreak in coal mines from historical sensor data under distribution drift. In: Yao, Y., Hu, Q., Yu, H., Grzymala-Busse, J.W. (eds.) RSFDGrC 2015. LNCS (LNAI), vol. 9437, pp. 439–451. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-25783-9_39CrossRef

45.

Boullé, M.: Predicting dangerous seismic events in coal mines under distribution drift. In: Ganzha, M., Maciaszek, L.A., Paprzycki, M. (eds.) Proceedings of FedCSIS 2016, pp. 227–230. IEEE (2016)

46.

Brahim, A.B., Limam, M.: Robust ensemble feature selection for high dimensional data sets. In: Proceedings of HPCS 2013, pp. 151–157 (2013)

47.

Bruni, R., Daraio, C., Aureli, D.: Imputation techniques for the reconstruction of missing interconnected data from higher educational institutions. Knowl.-Based Syst. 212, 106512 (2021). https://doi.org/10.1016/j.knosys.2020.106512CrossRef

48.

Bustince, H., et al.: On some classes of directionally monotone functions. Fuzzy Sets Syst. 386, 161–178 (2020). https://doi.org/10.1016/j.fss.2019.01.024MathSciNetCrossRefMATH

49.

Cai, W., et al.: A new seismic-based strain energy methodology for coal burst forecasting in underground coal mines. Int. J. Rock Mech. Min. Sci. 123, 104086 (2019). https://doi.org/10.1016/j.ijrmms.2019.104086CrossRef

50.

Cano, A., Krawczyk, B.: Kappa updated ensemble for drifting data stream mining. Mach. Learn. 109(1), 175–218 (2019). https://doi.org/10.1007/s10994-019-05840-zMathSciNetCrossRefMATH

51.

Cao, L., Chua, K.S., Chong, W.K., Lee, H.P., Gu, Q.M.: A comparison of PCA, KPCA and ICA for dimensionality reduction in support vector machine. Neurocomputing 55(1), 321–336 (2003). https://doi.org/10.1016/S0925-2312(03)00433-8CrossRef

52.

Carbone, P., Ewen, S., Fóra, G., Haridi, S., Richter, S., Tzoumas, K.: State management in apache Flink®: consistent stateful distributed stream processing. Proc. VLDB Endow. 10(12), 1718–1729 (2017). https://doi.org/10.14778/3137765.3137777CrossRef

53.

Carrizosa, E., Guerrero, V., Morales, D.R.: On mathematical optimization for the visualization of frequencies and adjacencies as rectangular maps. Eur. J. Oper. Res. 265(1), 290–302 (2018). https://doi.org/10.1016/j.ejor.2017.07.023MathSciNetCrossRefMATH

54.

Çekik, R., Uysal, A.K.: A novel filter feature selection method using rough set for short text data. Expert Syst. Appl. 160, 113691 (2020). https://doi.org/10.1016/j.eswa.2020.113691CrossRef

55.

Cen, Z., Wang, J.: Crude oil price prediction model with long short term memory deep learning based on prior knowledge data transfer. Energy 169, 160–171 (2019). https://doi.org/10.1016/j.energy.2018.12.016CrossRef

56.

Cerrada, M., Sánchez, R., Cabrera, D., Zurita, G., Li, C.: Multi-stage feature selection by using genetic algorithms for fault diagnosis in gearboxes based on vibration signal. Sensors 15(9), 23903–23926 (2015). https://doi.org/10.3390/s150923903CrossRef

57.

Cevallos Valdiviezo, H., Van Aelst, S.: Tree-based prediction on incomplete data using imputation or surrogate decisions. Inf. Sci. 311, 163–181 (2015). https://doi.org/10.1016/j.ins.2015.03.018CrossRef

58.

Chakraborty, D., Narayanan, V., Ghosh, A.: Integration of deep feature extraction and ensemble learning for outlier detection. Pattern Recognit. 89, 161–171 (2019). https://doi.org/10.1016/j.patcog.2019.01.002CrossRef

59.

Chalapathy, R., Chawla, S.: Deep learning for anomaly detection: a survey. CoRR abs/1901.03407 (2019)

60.

Chalapathy, R., Khoa, N.L.D., Chawla, S.: Robust deep learning methods for anomaly detection. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD 2020, pp. 3507–3508. Association for Computing Machinery, New York (2020). https://doi.org/10.1145/3394486.3406704

61.

Chandrashekar, G., Sahin, F.: A survey on feature selection methods. Comput. Electr. Eng. 40(1), 16–28 (2014)CrossRef

62.

Chądzyńska-Krasowska, A., Betliński, P., Ślęzak, D.: Scalable machine learning with granulated data summaries: a case of feature selection. In: Kryszkiewicz, M., Appice, A., Ślęzak, D., Rybinski, H., Skowron, A., Raś, Z.W. (eds.) ISMIS 2017. LNCS (LNAI), vol. 10352, pp. 519–529. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-60438-1_51CrossRef

63.

Che, J., Yang, Y., Li, L., Bai, X., Zhang, S., Deng, C.: Maximum relevance minimum common redundancy feature selection for nonlinear data. Inf. Sci. 409–410, 68–86 (2017). https://doi.org/10.1016/j.ins.2017.05.013CrossRefMATH

64.

Chen, L., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Trans. Pattern Anal. Mach. Intell. 40(4), 834–848 (2018). https://doi.org/10.1109/TPAMI.2017.2699184CrossRef

65.

Chen, S.-H., Du, Y.-R.: Granularity in economic decision making: an interdisciplinary review. In: Pedrycz, W., Chen, S.-M. (eds.) Granular Computing and Decision-Making. SBD, vol. 10, pp. 47–71. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-16829-6_3CrossRef

66.

Chen, T., Guestrin, C.: XGBoost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 785–794. Association for Computing Machinery, New York (2016). https://doi.org/10.1145/2939672.2939785

67.

Cheng, W., Dembczyński, K., Hüllermeier, E.: Graded multilabel classification: the ordinal case. In: Fürnkranz, J., Joachims, T. (eds.) Proceedings of the 27th International Conference on Machine Learning (ICML-10), 21–24 June 2010, Haifa, Israel, pp. 223–230. Omnipress (2010)

68.

Chlebus, B.S., Nguyen, S.H.: On finding optimal discretizations for two attributes. In: Polkowski, L., Skowron, A. (eds.) RSCTC 1998. LNCS (LNAI), vol. 1424, pp. 537–544. Springer, Heidelberg (1998). https://doi.org/10.1007/3-540-69115-4_74CrossRef

69.

Cho, K., et al.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. In: Moschitti, A., Pang, B., Daelemans, W. (eds.) Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, EMNLP 2014, 25–29 October 2014, Doha, Qatar, A meeting of SIGDAT, A Special Interest Group of the ACL, pp. 1724–1734. ACL (2014)

70.

Chu, C.T., et al.: Map-reduce for machine learning on multicore. In: Proceedings of NIPS, pp. 281–288 (2006)

71.

Ciucci, D., Yao, Y.: Synergy of granular computing, shadowed sets, and three-way decisions. Inf. Sci. 508, 422–425 (2020). https://doi.org/10.1016/j.ins.2019.09.003CrossRef

72.

Clark, P.G., Grzymała-Busse, J.W., Hippe, Z.S., Mroczek, T., Niemiec, R.: Complexity of rule sets mined from incomplete data using probabilistic approximations based on generalized maximal consistent blocks. Procedia Comput. Sci. 176, 1803–1812 (2020). https://doi.org/10.1016/j.procs.2020.09.219. Knowledge-Based and Intelligent Information & Engineering Systems: Proceedings of the 24th International Conference KES2020

73.

Collobert, R., Weston, J., Bottou, L., Karlen, M., Kavukcuoglu, K., Kuksa, P.P.: Natural language processing (almost) from scratch. J. Mach. Learn. Res. 12, 2493–2537 (2011). http://dl.acm.org/citation.cfm?id=2078186

74.

Cook, A.A., Misirli, G., Fan, Z.: Anomaly detection for IoT time-series data: a survey. IEEE Internet Things J. 7(7), 6481–6494 (2020). https://doi.org/10.1109/JIOT.2019.2958185CrossRef

75.

Cornelis, C., Jensen, R., Martín, G.H., Ślęzak, D.: Attribute selection with fuzzy decision reducts. Inf. Sci. 180(2), 209–224 (2010)MathSciNetMATHCrossRef

76.

Crochepierre, L., Boudjeloud-Assala, L., Barbesant, V.: Interpretable dimensionally-consistent feature extraction from electrical network sensors (2020). https://bitbucket.org/ghentdatascience/ecmlpkdd20-papers/raw/master/ADS/sub_795.pdf

77.

Lakshmipadmaja, D., Vishnuvardhan, B.: Classification performance improvement using random subset feature selection algorithm for data mining. Big Data Res. 12, 1–12 (2018). https://doi.org/10.1016/j.bdr.2018.02.007CrossRef

78.

Chelly Dagdia, Z., Zarges, C., Beck, G., Lebbah, M.: A scalable and effective rough set theory-based approach for big data pre-processing. Knowl. Inf. Syst. 62(8), 3321–3386 (2020). https://doi.org/10.1007/s10115-020-01467-yCrossRef

79.

Dai, J., Xu, Q.: Approximations and uncertainty measures in incomplete information systems. Inf. Sci. 198, 62–80 (2012)MathSciNetMATHCrossRef

80.

Dalal, N., Triggs, B., Schmid, C.: Human detection using oriented histograms of flow and appearance. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3952, pp. 428–441. Springer, Heidelberg (2006). https://doi.org/10.1007/11744047_33CrossRef

81.

Das, A.S., Datar, M., Garg, A., Rajaram, S.: Google news personalization: scalable online collaborative filtering. In: Proceedings of WWW, pp. 271–280 (2007)

82.

Das, S.: Filters, wrappers and a boosting-based hybrid for feature selection. In: Proceedings of ICML 2001, pp. 74–81 (2001)

83.

Dash, M., Liu, H.: Consistency-based search in feature selection. Artif. Intell. 151(1–2), 155–176 (2003)MathSciNetMATHCrossRef

84.

Dash, P.K., Nayak, M., Senapati, M.R., Lee, I.W.C.: Mining for similarities in time series data using wavelet-based feature vectors and neural networks. Eng. Appl. Artif. Intell. 20(2), 185–201 (2007). https://doi.org/10.1016/j.engappai.2006.06.018CrossRef

85.

Datar, M., Gionis, A., Indyk, P., Motwani, R.: Maintaining stream statistics over sliding windows. SIAM J. Comput. 31(6), 1794–1813 (2002)MathSciNetMATHCrossRef

86.

David, S.A., Machado, J.A.T., Trevisan, L.R., Inácio, C.M.C., Lopes, A.M.: Dynamics of commodities prices: integer and fractional models. Fundam. Inform. 151(1–4), 389–408 (2017). https://doi.org/10.3233/FI-2017-1499MathSciNetCrossRef

87.

Dayal, U., Castellanos, M., Simitsis, A., Wilkinson, K.: Data integration flows for business intelligence. In: Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology, EDBT 2009, pp. 1–11. ACM, New York (2009). https://doi.org/10.1145/1516360.1516362

88.

Dean, J., Ghemawat, S.: MapReduce: simplified data processing on large clusters. Commun. ACM 51(1), 107–113 (2008). https://doi.org/10.1145/1327452.1327492CrossRef

89.

Devlin, J., Chang, M., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. CoRR abs/1810.04805 (2018)

90.

Dietterich, T.G.: Ensemble methods in machine learning. In: Kittler, J., Roli, F. (eds.) MCS 2000. LNCS, vol. 1857, pp. 1–15. Springer, Heidelberg (2000). https://doi.org/10.1007/3-540-45014-9_1CrossRef

91.

Ding, C.H.Q., Peng, H.: Minimum redundancy feature selection from microarray gene expression data. J. Bioinform. Comput. Biol. 3(2), 185–206 (2005). https://doi.org/10.1142/S0219720005001004CrossRef

92.

Dobre, C., Xhafa, F.: Parallel programming paradigms and frameworks in big data era. Int. J. Parallel Prog. 42(5), 710–738 (2013). https://doi.org/10.1007/s10766-013-0272-7CrossRef

93.

Doherty, P., Szalas, A.: Rough set reasoning using answer set programs. Int. J. Approx. Reason. 130, 126–149 (2021). https://doi.org/10.1016/j.ijar.2020.12.010MathSciNetCrossRefMATH

94.

Domingos, P.: A few useful things to know about machine learning. Commun. ACM 55(10), 78–87 (2012)CrossRef

95.

Dong, H., Li, T., Ding, R., Sun, J.: A novel hybrid genetic algorithm with granular information for feature selection and optimization. Appl. Soft Comput. 65, 33–46 (2018). https://doi.org/10.1016/j.asoc.2017.12.048CrossRef

96.

Dou, L., Cai, W., Cao, A., Guo, W.: Comprehensive early warning of rock burst utilizing microseismic multi-parameter indices. Int. J. Min. Sci. Technol. 28(5), 767–774 (2018). https://doi.org/10.1016/j.ijmst.2018.08.007CrossRef

97.

Dougherty, J., Kohavi, R., Sahami, M.: Supervised and unsupervised discretization of continuous features. In: Proceedings of the Twelfth International Conference on International Conference on Machine Learning, ICML 1995, pp. 194–202. Morgan Kaufmann Publishers Inc., San Francisco (1995)

98.

Dramiński, M., Rada-Iglesias, A., Enroth, S., Wadelius, C., Koronacki, J., Komorowski, H.J.: Monte Carlo feature selection for supervised classification. Bioinformatics 24(1), 110–117 (2008)CrossRef

99.

Dubois, D.J., Casale, G.: OptiSpot: minimizing application deployment cost using spot cloud resources. Clust. Comput. 19(2), 893–909 (2016). https://doi.org/10.1007/s10586-016-0568-7CrossRef

100.

Dubois, D., Prade, H.: Rough fuzzy sets and fuzzy rough sets. Int. J. Gen. Syst. 17, 191–209 (1990). https://doi.org/10.1080/03081079008935107CrossRefMATH

101.

Duda, P., Rutkowski, L., Jaworski, M., Rutkowska, D.: On the Parzen kernel-based probability density function learning procedures over time-varying streaming data with applications to pattern classification. IEEE Trans. Cybern. 50(4), 1683–1696 (2020). https://doi.org/10.1109/TCYB.2018.2877611CrossRef

102.

Duforet-Frebourg, N., Luu, K., Laval, G., Bazin, E., Blum, M.G.: Detecting genomic signatures of natural selection with principal component analysis: application to the 1000 genomes data. Mol. Biol. Evol. 33(4), 1082–1093 (2015). https://doi.org/10.1093/molbev/msv334CrossRef

103.

Dutta, S., Jankowski, A., Rozenberg, G., Skowron, A.: Linking reaction systems with rough sets. Fundam. Informaticae 165(3–4), 283–302 (2019). https://doi.org/10.3233/FI-2019-1786MathSciNetCrossRefMATH

104.

Eiras-Franco, C., Bolón-Canedo, V., Ramos, S., González-Domínguez, J., Alonso-Betanzos, A., Touriño, J.: Multithreaded and Spark Parallelization of Feature Selection Filters. J. Comput. Sci. 17, 609–619 (2016)CrossRef

105.

Ekanayake, J., et al.: Twister: a runtime for iterative mapreduce. In: Proceedings of HPDC, pp. 810–818 (2010)

106.

Elmeleegy, K.: Piranha: optimizing short jobs in Hadoop. Proc. VLDB Endow. 6(11), 985–996 (2013)CrossRef

107.

Fan, J., Lv, J.: A selective overview of variable selection in high dimensional feature space. Stat. Sin. 20(1), 101–148 (2010)MathSciNetMATH

108.

Fayyad, U.M., Irani, K.B.: Multi-interval discretization of continuous-valued attributes for classification learning. In: IJCAI, pp. 1022–1029 (1993)

109.

Feng, J., Wang, E., Ding, H., Huang, Q., Chen, X.: Deterministic seismic hazard assessment of coal fractures in underground coal mine: a case study. Soil Dyn. Earthq. Eng. 129, 105921 (2020). https://doi.org/10.1016/j.soildyn.2019.105921CrossRef

110.

Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. Eur. J. Oper. Res. 270(2), 654–669 (2018). https://doi.org/10.1016/j.ejor.2017.11.054MathSciNetCrossRefMATH

111.

Fisher, A., Rudin, C., Dominici, F.: All models are wrong, but many are useful: learning a variable’s importance by studying an entire class of prediction models simultaneously. J. Mach. Learn. Res. 20(177), 1–81 (2019). http://jmlr.org/papers/v20/18-760.html

112.

Florescu, D., England, M.: Algorithmically generating new algebraic features of polynomial systems for machine learning. CoRR abs/1906.01455 (2019). http://arxiv.org/abs/1906.01455

113.

Fontes, C.H., Pereira, O.: Pattern recognition in multivariate time series - a case study applied to fault detection in a gas turbine. Eng. Appl. Artif. Intell. 49, 10–18 (2016). https://doi.org/10.1016/j.engappai.2015.11.005CrossRef

114.

Friedman, J.H.: Greedy function approximation: a gradient boosting machine. Ann. Stat. 29, 1189–1232 (2000)MathSciNetMATH

115.

Fu, T.C.: A review on time series data mining. Eng. Appl. Artif. Intell. 24(1), 164–181 (2011)CrossRef

116.

Fürnkranz, J., Hüllermeier, E., Loza Mencía, E., Brinker, K.: Multilabel classification via calibrated label ranking. Mach. Learn. 73(2), 133–153 (2008). https://doi.org/10.1007/s10994-008-5064-8CrossRefMATH

117.

Gao, W., Hu, L., Zhang, P.: Class-specific mutual information variation for feature selection. Pattern Recogn. 79, 328–339 (2018). https://doi.org/10.1016/j.patcog.2018.02.020CrossRef

118.

Garbulowski, M., et al.: R.ROSETTA: an interpretable machine learning framework. BMC Bioinform. 22(1), 110 (2021). https://doi.org/10.1186/s12859-021-04049-zCrossRef

119.

Garbulowski, M., et al.: Interpretable machine learning reveals dissimilarities between subtypes of autism spectrum disorder. Front. Genet. 12, 73 (2021). https://doi.org/10.3389/fgene.2021.618277CrossRef

120.

García, S., Luengo, J., Sáez, J.A., López, V., Herrera, F.: A survey of discretization techniques: taxonomy and empirical analysis in supervised learning. IEEE Trans. Knowl. Data Eng. 25(4), 734–750 (2013). https://doi.org/10.1109/TKDE.2012.35CrossRef

121.

García-Torres, M., Gómez-Vela, F., Melián-Batista, B., Moreno-Vega, J.M.: High-dimensional feature selection via feature grouping. Inf. Sci. 326, 102–118 (2016)CrossRef

122.

Geurts, P., Ernst, D., Wehenkel, L.: Extremely randomized trees. Mach. Learn. 63(1), 3–42 (2006). https://doi.org/10.1007/s10994-006-6226-1CrossRefMATH

123.

Ghosh, M., Guha, R., Sarkar, R., Abraham, A.: A wrapper-filter feature selection technique based on ant colony optimization. Neural Comput. Appl. 32(12), 7839–7857 (2019). https://doi.org/10.1007/s00521-019-04171-3CrossRef

124.

Gibowicz, S.J., Lasocki, S.: Seismicity induced by mining: 10 years later. In: Advances in Geophysics, pp. 81–164 (2001)

125.

Gill, S.S., et al.: Holistic resource management for sustainable and reliable cloud computing: an innovative solution to global challenge. J. Syst. Softw. 155, 104–129 (2019). https://doi.org/10.1016/j.jss.2019.05.025CrossRef

126.

Giuliani, A.: The application of principal component analysis to drug discovery and biomedical data. Drug Discov. Today 22(7), 1069–1076 (2017). https://doi.org/10.1016/j.drudis.2017.01.005CrossRef

127.

Goh, W.W.B., Wong, L.: Evaluating feature-selection stability in next-generation proteomics. J. Bioinform. Comput. Biol. 14(5), 1–23 (2016). https://doi.org/10.1142/S0219720016500293CrossRef

128.

Goldenberg, I., Webb, G.I.: Survey of distance measures for quantifying concept drift and shift in numeric data. Knowl. Inf. Syst. 60(2), 591–615 (2018). https://doi.org/10.1007/s10115-018-1257-zCrossRef

129.

González-Domínguez, J., Expósito, R.R., Bolón-Canedo, V.: CUDA-JMI: acceleration of feature selection on heterogeneous systems. Future Gener. Comput. Syst. 102, 426–436 (2020). https://doi.org/10.1016/j.future.2019.08.031CrossRef

130.

Govindan, P., Chen, R., Scheinberg, K., Srinivasan, S.: A scalable solution for group feature selection. In: Proceedings of IEEE Big Data 2015, pp. 2846–2848 (2015)

131.

Grave, E., Bojanowski, P., Gupta, P., Joulin, A., Mikolov, T.: Learning word vectors for 157 languages. CoRR abs/1802.06893 (2018). http://arxiv.org/abs/1802.06893

132.

Grochala, D., Kajor, M., Kucharski, D., Iwaniec, M., Kańtoch, E.: A novel approach in auscultation technology - new sensors and algorithms. In: Bujnowski, A., Kaczmarek, M., Ruminski, J. (eds.) 11th International Conference on Human System Interaction, HSI 2018, Gdansk, Poland, 4–6 July 2018, pp. 240–244. IEEE (2018). https://doi.org/10.1109/HSI.2018.8431339

133.

Grorud, L.J., Smith, D.: The national fire fighter near-miss reporting. Annual Report 2008. An Exclusive Supplement to Fire & Rescue Magazine, pp. 1–24 (2008)

134.

Grużdź, A., Ihnatowicz, A., Ślęzak, D.: Interactive gene clustering - a case study of breast cancer microarray data. Inf. Syst. Front. 8(1), 21–27 (2006). https://doi.org/10.1007/s10796-005-6100-xCrossRef

135.

Grychowski, T.: Hazard assessment based on fuzzy logic. Arch. Min. Sci. 53(4), 595–602 (2008)

136.

Grzegorowski, M.: Scaling of complex calculations over big data-sets. In: Ślezak, D., Schaefer, G., Vuong, S.T., Kim, Y.-S. (eds.) AMT 2014. LNCS, vol. 8610, pp. 73–84. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-09912-5_7CrossRef

137.

Grzegorowski, M.: Governance of the redundancy in the feature selection based on rough sets’ reducts. In: Flores, V., et al. (eds.) IJCRS 2016. LNCS (LNAI), vol. 9920, pp. 548–557. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-47160-0_50CrossRef

138.

Grzegorowski, M.: Massively parallel feature extraction framework application in predicting dangerous seismic events. In: Proceedings of FedCSIS 2016, pp. 225–229 (2016)

139.

Grzegorowski, M.: Selected aspects of interactive feature extraction. Ph.D. thesis, University of Warsaw (2021)

140.

Grzegorowski, M., Janusz, A., Lazewski, S., Swiechowski, M., Jankowska, M.: Prescriptive analytics for optimization of FMCG delivery plans. In: Ciucci, D., et al. (eds.) IPMU 2022. Computer and Information Science, vol. 1602, pp. 44–53. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-08974-9_4CrossRef

141.

Grzegorowski, M., Janusz, A., Ślęzak, D., Szczuka, M.S.: On the role of feature space granulation in feature selection processes. In: Nie, J., et al. (eds.) 2017 IEEE International Conference on Big Data, BigData 2017, Boston, MA, USA, 11–14 December 2017, pp. 1806–1815. IEEE Computer Society (2017). https://doi.org/10.1109/BigData.2017.8258124

142.

Grzegorowski, M., Kalisch, M., Kozielski, M., Wróbel, Ł.: Hurtowania danych i procesy ETL. In: Przystałka, P., Sikora, M. (eds.) Zintegrowany, szkieletowy system wspmagania decyzji dla systemów monitorowania procesów, urządzeń i zagrożeń, chap. 3, pp. 31–40. Monograficzna Seria Wydawnicza Instyututu Technik Innowacyjnych EMAG (2017). http://disesor.ibemag.pl/www/disesor.ibemag.pl/data/Image/hurtownia.pdf

143.

Grzegorowski, M., Litwin, J., Wnuk, M., Pabis, M., Marcinowski, L.: Survival-based feature extraction - application in supply management for dispersed vending machines. IEEE Trans. Industr. Inform. (2022). https://doi.org/10.1109/TII.2022.3178547CrossRef

144.

Grzegorowski, M., Pardel, P.W., Stawicki, S., Stencel, K.: SONCA: scalable semantic processing of rapidly growing document stores. In: Pechenizkiy, M., Wojciechowski, M. (eds.) New Trends in Databases and Information Systems. Advances in Intelligent Systems and Computing, vol. 185, pp. 89–98. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-32518-2_9CrossRef

145.

Grzegorowski, M., Ślęzak, D.: On resilient feature selection: computational foundations of r-C-reducts. Inf. Sci. 499, 25–44 (2019). https://doi.org/10.1016/j.ins.2019.05.041MathSciNetCrossRefMATH

146.

Grzegorowski, M., Stawicki, S.: Window-based feature engineering for prediction of methane threats in coal mines. In: Yao, Y., Hu, Q., Yu, H., Grzymala-Busse, J.W. (eds.) RSFDGrC 2015. LNCS (LNAI), vol. 9437, pp. 452–463. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-25783-9_40CrossRef

147.

Grzegorowski, M., Stawicki, S.: Window-based feature extraction framework for multi-sensor data: a posture recognition case study. In: Ganzha, M., Maciaszek, L.A., Paprzycki, M. (eds.) 2015 Federated Conference on Computer Science and Information Systems, FedCSIS 2015, Lódz, Poland, 13–16 September 2015, pp. 397–405. IEEE (2015). https://doi.org/10.15439/2015F425

148.

Grzegorowski, M., Zdravevski, E., Janusz, A., Lameski, P., Apanowicz, C., Ślęzak, D.: Cost optimization for big data workloads based on dynamic scheduling and cluster-size tuning. Big Data Res. 25, 100203 (2021). https://doi.org/10.1016/j.bdr.2021.100203CrossRef

149.

Gu, B., Liu, G., Huang, H.: Groups-keeping solution path algorithm for sparse regression with automatic feature grouping. In: Proceedings of the KDD, pp. 185–193 (2017)

150.

Guo, Y., Zhou, Y., Zhang, Z.: Fault diagnosis of multi-channel data by the CNN with the multilinear principal component analysis. Measurement 171, 108513 (2020). https://doi.org/10.1016/j.measurement.2020.108513CrossRef

151.

Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. J. Mach. Learn. Res. 3, 1157–1182 (2003)MATH

152.

Guyon, I., Nikravesh, M., Gunn, S., Zadeh, L.A. (eds.): Feature Extraction. Studies in Fuzziness and Soft Computing, vol. 207. Springer, Heidelberg (2006). https://doi.org/10.1007/978-3-540-35488-8CrossRef

153.

Güzel, B.E.K., Karaçalı, B.: Fisher’s linear discriminant analysis based prediction using transient features of seismic events in coal mines. In: Ganzha, M., Maciaszek, L., Paprzycki, M. (eds.) Proceedings of the 2016 Federated Conference on Computer Science and Information Systems. Annals of Computer Science and Information Systems, vol. 8, pp. 231–234. IEEE (2016). https://doi.org/10.15439/2016F116

154.

Ha, S., Choi, S.: Convolutional neural networks for human activity recognition using multiple accelerometer and gyroscope sensors. In: 2016 International Joint Conference on Neural Networks (IJCNN), pp. 381–388 (2016). https://doi.org/10.1109/IJCNN.2016.7727224

155.

Hall, M.: Correlation-based feature selection for machine learning. Ph.D. thesis, University of Waikato (1999)

156.

Hamed, A., Sobhy, A., Nassar, H.: Distributed approach for computing rough set approximations of big incomplete information systems. Inf. Sci. 547, 427–449 (2021). https://doi.org/10.1016/j.ins.2020.08.049MathSciNetCrossRefMATH

157.

Hancer, E.: Differential evolution for feature selection: a fuzzy wrapper–filter approach. Soft. Comput. 23(13), 5233–5248 (2018). https://doi.org/10.1007/s00500-018-3545-7CrossRef

158.

Hancer, E., Xue, B., Zhang, M.: Differential evolution for filter feature selection based on information theory and feature ranking. Knowl. Based Syst. 140, 103–119 (2018). https://doi.org/10.1016/j.knosys.2017.10.028CrossRef

159.

Hariri, R.H., Fredericks, E.M., Bowers, K.M.: Uncertainty in big data analytics: survey, opportunities, and challenges. J. Big Data 6(1), 1–16 (2019). https://doi.org/10.1186/s40537-019-0206-3CrossRef

160.

He, Y.L., Tian, Y., Xu, Y., Zhu, Q.X.: Novel soft sensor development using echo state network integrated with singular value decomposition: application to complex chemical processes. Chemometr. Intell. Lab. Syst. 200, 103981 (2020)CrossRef

161.

Heidari, A.A., Mirjalili, S., Faris, H., Aljarah, I., Mafarja, M.M., Chen, H.: Harris hawks optimization: algorithm and applications. Future Gener. Comput. Syst. 97, 849–872 (2019). https://doi.org/10.1016/j.future.2019.02.028CrossRef

162.

Herodotou, H., Dong, F., Babu, S.: No one (cluster) size fits all: automatic cluster sizing for data-intensive analytics. In: Proceedings of the 2nd ACM Symposium on Cloud Computing, p. 18. ACM (2011)

163.

Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality of data with neural networks. Science 313(5786), 504–507 (2006). https://doi.org/10.1126/science.1127647. http://science.sciencemag.org/content/313/5786/504

164.

Hirota, K.: Concepts of probabilistic sets. Fuzzy Sets Syst. 5(1), 31–46 (1981). https://doi.org/10.1016/0165-0114(81)90032-4MathSciNetCrossRefMATH

165.

Hońko, P.: Attribute reduction: a horizontal data decomposition approach. Soft. Comput. 20(3), 951–966 (2016). https://doi.org/10.1007/s00500-014-1554-8CrossRefMATH

166.

Hosseini, B., Hammer, B.: Interpretable discriminative dimensionality reduction and feature selection on the manifold. In: Brefeld, U., Fromont, E., Hotho, A., Knobbe, A., Maathuis, M., Robardet, C. (eds.) ECML PKDD 2019. LNCS (LNAI), vol. 11906, pp. 310–326. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-46150-8_19CrossRef

167.

Hu, L., Zhang, Z. (eds.): EEG Signal Processing and Feature Extraction. Springer, Singapore (2019). https://doi.org/10.1007/978-981-13-9113-2CrossRef

168.

Hu, X.: Ensembles of classifiers based on rough sets theory and set-oriented database operations. In: Proceedings of IEEE GrC 2006, pp. 67–73 (2006)

169.

Hua, Q.-S., Yu, D., Lau, F.C.M., Wang, Y.: Exact algorithms for set multicover and multiset multicover problems. In: Dong, Y., Du, D.-Z., Ibarra, O. (eds.) ISAAC 2009. LNCS, vol. 5878, pp. 34–44. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-10631-6_6CrossRef

170.

Huang, Q., Cao, J.: Research on coal pillar malposition distance based on coupling control of three-field in shallow buried closely spaced multi-seam mining, China. Energies 12(3), 462 (2019). https://doi.org/10.3390/en12030462CrossRef

171.

Huang, X., Zhang, L., Wang, B., Li, F., Zhang, Z.: Feature clustering based support vector machine recursive feature elimination for gene selection. Appl. Intell. 48(3), 594–607 (2017). https://doi.org/10.1007/s10489-017-0992-2CrossRef

172.

Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: Proceedings of the 32nd International Conference on International Conference on Machine Learning - Volume 37, ICML 2015, pp. 448–456. JMLR.org (2015). http://dl.acm.org/citation.cfm?id=3045118.3045167

173.

Irizarry, R.A., et al.: Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics 4(2), 249–264 (2003). https://doi.org/10.1093/biostatistics/4.2.249MathSciNetCrossRefMATH

174.

Islam, M.T., Srirama, S.N., Karunasekera, S., Buyya, R.: Cost-efficient dynamic scheduling of big data applications in apache spark on cloud. J. Syst. Softw. 162, 110515 (2020). https://doi.org/10.1016/j.jss.2019.110515CrossRef

175.

Jain, A.K., Dubes, R.C.: Algorithms for Clustering Data. Prentice-Hall Inc, New Jersey (1988)MATH

176.

Jain, I., Jain, V.K., Jain, R.: Correlation feature selection based improved-binary particle swarm optimization for gene selection and cancer classification. Appl. Soft Comput. 62, 203–215 (2018). https://doi.org/10.1016/j.asoc.2017.09.038CrossRef

177.

Jankowski, A., Skowron, A., Swiniarski, R.W.: Interactive complex granules. Fundam. Inform. 133(2–3), 181–196 (2014). https://doi.org/10.3233/FI-2014-1070CrossRef

178.

Janusz, A.: Algorithms for similarity relation learning from high dimensional data. Ph.D. thesis, University of Warsaw (2014)

179.

Janusz, A.: Algorithms for similarity relation learning from high dimensional data. In: Peters, J.F., Skowron, A. (eds.) Transactions on Rough Sets XVII. LNCS, vol. 8375, pp. 174–292. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-642-54756-0_7CrossRef

180.

Janusz, A., Grad, Ł., Grzegorowski, M.: Clash Royale challenge: how to select training decks for win-rate prediction. In: Ganzha, M., Maciaszek, L.A., Paprzycki, M. (eds.) Proceedings of the 2019 Federated Conference on Computer Science and Information Systems, FedCSIS 2019, Leipzig, Germany, 1–4 September 2019. Annals of Computer Science and Information Systems, vol. 18, pp. 3–6 (2019). https://doi.org/10.15439/2019F365

181.

Janusz, A., et al.: Przykłady zastosowania systemu DISESOR w analizie i predykcji zagrożeń. In: Przystałka, P., Sikora, M. (eds.) Zintegrowany, szkieletowy system wspmagania decyzji dla systemów monitorowania procesów, urządzeń i zagrożeń, chap. 11, pp. 31–40. Monograficzna Seria Wydawnicza Instyututu Technik Innowacyjnych EMAG (2017). http://disesor.ibemag.pl/www/disesor.ibemag.pl/data/Image/przyklad-predykcja.pdf

182.

Janusz, A., Grzegorowski, M., Michalak, M., Wróbel, Ł, Sikora, M., Ślęzak, D.: Predicting seismic events in coal mines based on underground sensor measurements. Eng. Appl. Artif. Intell. 64, 83–94 (2017)CrossRef

183.

Janusz, A., Krasuski, A., Stawicki, S., Rosiak, M., Ślęzak, D., Nguyen, H.S.: Key risk factors for polish state fire service: a data mining competition at knowledge pit. In: Ganzha, M., Maciaszek, L.A., Paprzycki, M. (eds.) Proceedings of the 2014 Federated Conference on Computer Science and Information Systems, Warsaw, Poland, 7–10 September 2014. Annals of Computer Science and Information Systems, vol. 2, pp. 345–354 (2014). https://doi.org/10.15439/2014F507

184.

Janusz, A., et al.: Mining data from coal mines: IJCRS’15 data challenge. In: Yao, Y., Hu, Q., Yu, H., Grzymala-Busse, J.W. (eds.) RSFDGrC 2015. LNCS (LNAI), vol. 9437, pp. 429–438. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-25783-9_38CrossRef

185.

Janusz, A., Ślęzak, D.: Rough set methods for attribute clustering and selection. Appl. Artif. Intell. 28(3), 220–242 (2014). https://doi.org/10.1080/08839514.2014.883902CrossRef

186.

Janusz, A., Ślęzak, D.: Computation of approximate Reducts with dynamically adjusted approximation threshold. In: Esposito, F., Pivert, O., Hacid, M.-S., Raś, Z.W., Ferilli, S. (eds.) ISMIS 2015. LNCS (LNAI), vol. 9384, pp. 19–28. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-25252-0_3CrossRef

187.

Janusz, A., Ślęzak, D., Sikora, M., Wróbel, Ł.: Predicting dangerous seismic events: AAIA’16 data mining challenge. In: Ganzha, M., Maciaszek, L.A., Paprzycki, M. (eds.) Proceedings of the 2016 Federated Conference on Computer Science and Information Systems, FedCSIS 2016, Gdańsk, Poland, 11–14 September 2016. Annals of Computer Science and Information Systems, vol. 8, pp. 205–211. IEEE (2016). https://doi.org/10.15439/2016F560

188.

Janusz, A., Szczuka, M.S.: Assessment of data granulations in context of feature extraction problem. In: Proceedings of IEEE GrC, pp. 116–120 (2014)

189.

Janusz, A., Tajmajer, T., Świechowski, M.: Helping AI to play hearthstone: AAIA’17 data mining challenge. In: Proceedings of FedCSIS, pp. 121–125 (2017)

190.

Jelodar, H., et al.: Latent Dirichlet allocation (LDA) and topic modeling: models, applications, a survey. Multimed. Tools Appl. 78(11), 15169–15211 (2018). https://doi.org/10.1007/s11042-018-6894-4CrossRef

191.

Jia, X., Shang, L., Zhou, B., Yao, Y.: Generalized attribute Reduct in rough set theory. Knowl. Based Syst. 91, 204–218 (2016). https://doi.org/10.1016/j.knosys.2015.05.017CrossRef

192.

Jiménez, F., Palma, J.T., Sánchez, G., Marín, D., Ortega, F.P., López, M.D.L.: Feature selection based multivariate time series forecasting: an application to antibiotic resistance outbreaks prediction. Artif. Intell. Med. 104, 101818 (2020)CrossRef

193.

Jin, R., Si, L.: A study of methods for normalizing user ratings in collaborative filtering. In: Proceedings of the 27th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2004, pp. 568–569. ACM, New York (2004). https://doi.org/10.1145/1008992.1009124. http://doi.acm.org/10.1145/1008992.1009124

194.

Jin, R., Si, L., Zhai, C., Callan, J.: Collaborative filtering with decoupled models for preferences and ratings. In: Proceedings of the Twelfth International Conference on Information and Knowledge Management, CIKM 2003, pp. 309–316. ACM, New York (2003). https://doi.org/10.1145/956863.956922. http://doi.acm.org/10.1145/956863.956922

195.

Jing, Y., Li, T., Fujita, H., Wang, B., Cheng, N.: An incremental attribute reduction method for dynamic data mining. Inf. Sci. 465, 202–218 (2018). https://doi.org/10.1016/j.ins.2018.07.001MathSciNetCrossRefMATH

196.

Jing, Y., Li, T., Luo, C., Horng, S.J., Wang, G., Yu, Z.: An incremental approach for attribute reduction based on knowledge granularity. Knowl. Based Syst. 104, 24–38 (2016)CrossRef

197.

Jovic, A., Brkic, K., Bogunovic, N.: A review of feature selection methods with applications. In: Proceedings of MIPRO 2015, pp. 1200–1205 (2015)

198.

Kabiesz, J.: Effect of the form of data on the quality of mine tremors hazard forecasting using neural networks. Geotech. Geol. Eng. 24(5), 1131–1147 (2006). https://doi.org/10.1007/s10706-005-1136-8CrossRef

199.

Kabiesz, J.: The justification and objective to modify methods of forecasting the potential and assess the actual state of rockburst hazard. In: Methods for Assessment of Rockburst Hazard in Coal Mines’ Excavations, vol. 44, pp. 44–48 (2010). (in Polish)

200.

Kabiesz, J., Sikora, B., Sikora, M., Wróbel, Ł: Application of rule-based models for seismic hazard prediction in coal mines. Acta Montanistica Slovaca 18(3), 262–277 (2013)

201.

Kalousis, A., Prados, J., Hilario, M.: Stability of feature selection algorithms: a study on high-dimensional spaces. Knowl. Inf. Syst. 12(1), 95–116 (2007)CrossRef

202.

Kang, M., Tian, J.: Machine Learning: Data Pre-processing, pp. 111–130 (2019). https://doi.org/10.1002/9781119515326.ch5

203.

Kańtoch, E., Augustyniak, P., Markiewicz, M., Prusak, D.: Monitoring activities of daily living based on wearable wireless body sensor network. In: 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014, Chicago, IL, USA, 26–30 August 2014, pp. 586–589. IEEE (2014). https://doi.org/10.1109/EMBC.2014.6943659

204.

Kántoch, E., Grochala, D., Kajor, M., Kucharski, D.: The prototype of wearable sensors system for supervision of patient rehabilitation using artificial intelligence methods. In: IBE 2017. AISC, vol. 623, pp. 205–214. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-70063-2_22CrossRef

205.

Karabatak, M., Ince, M.C.: A new feature selection method based on association rules for diagnosis of erythemato-squamous diseases. Expert Syst. Appl. 36(10), 12500–12505 (2009)CrossRef

206.

Kasinikota, A., Balamurugan, P., Shevade, S.: Modeling label interactions in multi-label classification: a multi-structure SVM perspective. In: Phung, D., Tseng, V.S., Webb, G.I., Ho, B., Ganji, M., Rashidi, L. (eds.) PAKDD 2018. LNCS (LNAI), vol. 10937, pp. 43–55. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93034-3_4CrossRef

207.

Kaufman, S., Rosset, S., Perlich, C., Stitelman, O.: Leakage in data mining: formulation, detection, and avoidance. TKDD 6(4), 15 (2012). https://doi.org/10.1145/2382577.2382579. http://doi.acm.org/10.1145/2382577.2382579

208.

Kaur, N., Sood, S.K.: Efficient resource management system based on 4Vs of big data streams. Big Data Res. 9, 98–106 (2017). https://doi.org/10.1016/j.bdr.2017.02.002CrossRef

209.

Keller, R., Häfner, L., Sachs, T., Fridgen, G.: Scheduling flexible demand in cloud computing spot markets. Bus. Inf. Syst. Eng. 62(1), 25–39 (2019). https://doi.org/10.1007/s12599-019-00592-5CrossRef

210.

Keogh, E., Lin, J., Fu, A.: Hot sax: efficiently finding the most unusual time series subsequence. In: Proceedings of the Fifth IEEE International Conference on Data Mining, ICDM 2005, pp. 226–233. IEEE Computer Society, Washington, DC (2005). https://doi.org/10.1109/ICDM.2005.79

211.

Keogh, E.J., Pazzani, M.J.: Scaling up dynamic time warping for datamining applications. In: Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2000, pp. 285–289. ACM, New York (2000). https://doi.org/10.1145/347090.347153

212.

Keren, G., Schuller, B.W.: Convolutional RNN: an enhanced model for extracting features from sequential data. In: 2016 International Joint Conference on Neural Networks, IJCNN 2016, Vancouver, BC, Canada, 24–29 July 2016, pp. 3412–3419. IEEE (2016). https://doi.org/10.1109/IJCNN.2016.7727636

213.

Khandelwal, V., Chaturvedi, A.K., Gupta, C.P.: Amazon EC2 spot price prediction using regression random forests. IEEE Trans. Cloud Comput. 8(1), 59–72 (2020)CrossRef

214.

Kieu, T., Yang, B., Guo, C., Jensen, C.S.: Outlier detection for time series with recurrent autoencoder ensembles. In: Kraus, S. (ed.) Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI 2019, Macao, China, 10–16 August 2019, pp. 2725–2732. ijcai.org (2019). https://doi.org/10.24963/ijcai.2019/378

215.

Kin-Pong Chan, F., Wai-chee Fu, A., Yu, C.: Haar wavelets for efficient similarity search of time-series: with and without time warping. IEEE Trans. Knowl. Data Eng. 15(3), 686–705 (2003). https://doi.org/10.1109/TKDE.2003.1198399

216.

Kobak, D., Berens, P.: The art of using t-SNE for single-cell transcriptomics. Nature Commun. 10, 1–14 (2019). https://doi.org/10.1038/s41467-019-13056-xCrossRef

217.

Kornowski, J.: Linear prediction of aggregated seismic and seismoacoustic energy emitted from a mining longwall. Acta Montana Ser. A 22(129), 5–14 (2003)

218.

Kowalski, M., Ślęzak, D., Stencel, K., Pardel, P.W., Grzegorowski, M., Kijowski, M.: RDBMS model for scientific articles analytics. In: Bembenik, R., Skonieczny, L., Rybiński, H., Niezgodka, M. (eds.) Intelligent Tools for Building a Scientific Information Platform. Studies in Computational Intelligence, vol. 390, pp. 49–60. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-24809-2_4

219.

Kozielski, M., Sikora, M., Wróbel, Ł.: DISESOR - decision support system for mining industry. In: Ganzha, M., Maciaszek, L.A., Paprzycki, M. (eds.) 2015 Federated Conference on Computer Science and Information Systems, FedCSIS 2015, Lódz, Poland, 13–16 September 2015. Annals of Computer Science and Information Systems, vol. 5, pp. 67–74. IEEE (2015). https://doi.org/10.15439/2015F168

220.

Krawczyk, B., Minku, L.L., Gama, J., Stefanowski, J., Woźniak, M.: Ensemble learning for data stream analysis: a survey. Inf. Fusion 37, 132–156 (2017). https://doi.org/10.1016/j.inffus.2017.02.004CrossRef

221.

Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Pereira, F., Burges, C.J.C., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems 25, pp. 1097–1105. Curran Associates, Inc. (2012)

222.

Kryszkiewicz, M.: Rough set approach to incomplete information systems. Inf. Sci. 112(1), 39–49 (1998). https://doi.org/10.1016/S0020-0255(98)10019-1MathSciNetCrossRefMATH

223.

Kuncheva, L.I., Diez, J.J.R.: On feature selection protocols for very low-sample-size data. Pattern Recognit. 81, 660–673 (2018). https://doi.org/10.1016/j.patcog.2018.03.012CrossRef

224.

Kurach, K., Pawłowski, K.: Predicting dangerous seismic activity with recurrent neural networks. In: Ganzha, M., Maciaszek, L., Paprzycki, M. (eds.) Proceedings of the 2016 Federated Conference on Computer Science and Information Systems. Annals of Computer Science and Information Systems, vol. 8, pp. 239–243. IEEE (2016). https://doi.org/10.15439/2016F134

225.

Kusuma, R.M.I., Ho, T.T., Kao, W.C., Ou, Y.Y., Hua, K.L.: Using deep learning neural networks and candlestick chart representation to predict stock market (2019)

226.

Lameski, P., Zdravevski, E., Mingov, R., Kulakov, A.: SVM parameter tuning with grid search and its impact on reduction of model over-fitting. In: Yao, Y., Hu, Q., Yu, H., Grzymala-Busse, J.W. (eds.) RSFDGrC 2015. LNCS (LNAI), vol. 9437, pp. 464–474. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-25783-9_41CrossRef

227.

Lan, G., Hou, C., Nie, F., Luo, T., Yi, D.: Robust feature selection via simultaneous sapped norm and sparse regularizer minimization. Neurocomputing 283, 228–240 (2018)CrossRef

228.

Landfors, M., Philip, P., Rydén, P., Stenberg, P.: Normalization of high dimensional genomics data where the distribution of the altered variables is skewed. PLOS ONE 6(11), 1–11 (11 2011). https://doi.org/10.1371/journal.pone.0027942. https://doi.org/10.1371/journal.pone.0027942

229.

Laptev, I., Marszalek, M., Schmid, C., Rozenfeld, B.: Learning realistic human actions from movies. In: 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2008), 24–26 June 2008, Anchorage, Alaska, USA. IEEE Computer Society (2008). https://doi.org/10.1109/CVPR.2008.4587756

230.

Lara, O.D., Labrador, M.A.: A survey on human activity recognition using wearable sensors. IEEE Commun. Surv. Tutorials 15(3), 1192–1209 (2013). https://doi.org/10.1109/SURV.2012.110112.00192CrossRef

231.

Lasocki, S.: Probabilistic analysis of seismic hazard posed by mining induced events. In: Proceedings of Sixth International Symposium on Rockburst and Seismicity in Mines, pp. 151–156 (2005)

232.

Lazar, C., et al.: A survey on filter techniques for feature selection in gene expression microarray analysis. IEEE ACM Trans. Comput. Biol. Bioinform. 9(4), 1106–1119 (2012). https://doi.org/10.1109/TCBB.2012.33CrossRef

233.

LeCun, Y., Kavukcuoglu, K., Farabet, C.: Convolutional networks and applications in vision. In: ISCAS, pp. 253–256. IEEE (2010)

234.

Lee, K.H., Lee, Y.J., Choi, H., Chung, Y.D., Moon, B.: Parallel data processing with mapreduce: a survey. SIGMOD Rec. 40(4), 11–20 (2012)CrossRef

235.

Leśniak, A., Isakow, Z.: Space-time clustering of seismic events and hazard assessment in the Zabrze-Bielszowice coal mine, Poland. Int. J. Rock Mech. Min. Sci. 46(5), 918–928 (2009). https://doi.org/10.1016/j.ijrmms.2008.12.003CrossRef

236.

Levada, A.L.: Parametric PCA for unsupervised metric learning. Pattern Recogn. Lett. 135, 425–430 (2020). https://doi.org/10.1016/j.patrec.2020.05.011CrossRef

237.

Li, C., Ai, D.: Automatic crack detection method for loaded coal in vibration failure process. PLOS ONE 12(10), 1–21 (2017). https://doi.org/10.1371/journal.pone.0185750CrossRef

238.

Li, M., Hinnov, L., Kump, L.: Acycle: time-series analysis software for paleoclimate research and education. Comput. Geosci. 127, 12–22 (2019). https://doi.org/10.1016/j.cageo.2019.02.011CrossRef

239.

Li, P., Wu, J., Shang, L.: Fast approximate attribute reduction with MapReduce. In: Proceedings of RSKT 2013, pp. 271–278 (2013)

240.

Lin, C.C., Deng, D.J., Kuo, C.H., Chen, L.: Concept drift detection and adaption in big imbalance industrial IoT data using an ensemble learning method of offline classifiers. IEEE Access 7, 56198–56207 (2019). https://doi.org/10.1109/ACCESS.2019.2912631CrossRef

241.

Lin, J., Vlachos, M., Keogh, E., Gunopulos, D.: Iterative incremental clustering of time series. In: Bertino, E., et al. (eds.) EDBT 2004. LNCS, vol. 2992, pp. 106–122. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-24741-8_8CrossRef

242.

Lin, W.-C., Tsai, C.-F.: Missing value imputation: a review and analysis of the literature (2006–2017). Artif. Intell. Rev. 53(2), 1487–1509 (2019). https://doi.org/10.1007/s10462-019-09709-4CrossRef

243.

Liu, A., Lu, J., Liu, F., Zhang, G.: Accumulating regional density dissimilarity for concept drift detection in data streams. Pattern Recogn. 76, 256–272 (2018). https://doi.org/10.1016/j.patcog.2017.11.009CrossRef

244.

Liu, B., Tsoumakas, G.: Dealing with class imbalance in classifier chains via random undersampling. Knowl.-Based Syst. 192, 105292 (2020). https://doi.org/10.1016/j.knosys.2019.105292CrossRef

245.

Liu, H., Motoda, H. (eds.): Feature Extraction, Construction and Selection. Springer, Heidelberg (1998). https://doi.org/10.1007/978-1-4615-5725-8CrossRefMATH

246.

Liu, H., Wu, X., Zhang, S.: A new supervised feature selection method for pattern classification. Comput. Intell. 30(2), 342–361 (2014)MathSciNetCrossRef

247.

Liu, J., Wang, S., Yang, W.: Sparse autoencoder for social image understanding. Neurocomputing 369, 122–133 (2019). https://doi.org/10.1016/j.neucom.2019.08.083CrossRef

248.

Liu, K., Yang, X., Yu, H., Mi, J., Wang, P., Chen, X.: Rough set based semi-supervised feature selection via ensemble selector. Knowl. Based Syst. 165, 282–296 (2019). https://doi.org/10.1016/j.knosys.2018.11.034CrossRef

249.

Liu, X., Pedrycz, W.: The development of fuzzy decision trees in the framework of axiomatic fuzzy set logic. Appl. Soft Comput. 7(1), 325–342 (2007). https://doi.org/10.1016/j.asoc.2005.07.003CrossRef

250.

Liu, Y., Gao, X., Gao, Q., Shao, L., Han, J.: Adaptive robust principal component analysis. Neural Netw. 119, 85–92 (2019). https://doi.org/10.1016/j.neunet.2019.07.015CrossRefMATH

251.

Luo, C., Li, T., Yao, Y.: Dynamic probabilistic rough sets with incomplete data. Inf. Sci. 417, 39–54 (2017). https://doi.org/10.1016/j.ins.2017.06.040CrossRef

252.

Luo, J., Fujita, H., Yao, Y., Qin, K.: On modeling similarity and three-way decision under incomplete information in rough set theory. Knowl.-Based Syst. 191, 105251 (2020). https://doi.org/10.1016/j.knosys.2019.105251CrossRef

253.

Luts, J., Ojeda, F., de Plas, R.V., Moor, B.D., Huffel, S.V., Suykens, J.A.: A tutorial on support vector machine-based methods for classification problems in chemometrics. Anal. Chim. Acta 665(2), 129–145 (2010). https://doi.org/10.1016/j.aca.2010.03.030CrossRef

254.

Ma, C., Li, W., Cao, J., Du, J., Li, Q., Gravina, R.: Adaptive sliding window based activity recognition for assisted livings. Inf. Fusion 53, 55–65 (2020). https://doi.org/10.1016/j.inffus.2019.06.013CrossRef

255.

Mafarja, M.M., Mirjalili, S.: Hybrid whale optimization algorithm with simulated annealing for feature selection. Neurocomputing 260, 302–312 (2017). https://doi.org/10.1016/j.neucom.2017.04.053CrossRef

256.

Mafarja, M.M., Mirjalili, S.: Hybrid binary ant lion optimizer with rough set and approximate entropy reducts for feature selection. Soft. Comput. 23(15), 6249–6265 (2018). https://doi.org/10.1007/s00500-018-3282-yCrossRef

257.

Maldonado, S., López, J.: Dealing with high-dimensional class-imbalanced datasets: Embedded feature selection for SVM classification. Appl. Soft Comput. 67, 94–105 (2018). https://doi.org/10.1016/j.asoc.2018.02.051CrossRef

258.

Malondkar, A., Corizzo, R., Kiringa, I., Ceci, M., Japkowicz, N.: Spark-GHSOM: growing hierarchical self-organizing map for large scale mixed attribute datasets. Inf. Sci. 496, 572–591 (2019). https://doi.org/10.1016/j.ins.2018.12.007CrossRef

259.

Manning, C.D., Raghavan, P., Schütze, H.: Introduction to Information Retrieval. Cambridge University Press, Cambridge (2008). https://doi.org/10.1017/CBO9780511809071. https://nlp.stanford.edu/IR-book/pdf/irbookprint.pdf

260.

Mark, C.: Coal bursts in the deep longwall mines of the United States. Int. J. Coal Sci. Technol. 3(1), 1–9 (2016)MathSciNetCrossRef

261.

Mason, A.J.: Bayesian methods for modelling non-random missing data mechanisms in longitudinal studies. Ph.D. thesis, Imperial College London (2009)

262.

Mathew, S.: Overview of Amazon Web Services, April 2017. Accessed 04 June 2019

263.

Meina, M., Janusz, A., Rykaczewski, K., Ślęzak, D., Celmer, B., Krasuski, A.: Tagging firefighter activities at the emergency scene: summary of AAIA’15 data mining competition at knowledge pit. In: Ganzha, M., Maciaszek, L.A., Paprzycki, M. (eds.) 2015 Federated Conference on Computer Science and Information Systems, FedCSIS 2015, Lódz, Poland, 13–16 September 2015. Annals of Computer Science and Information Systems, vol. 5, pp. 367–373. IEEE (2015). https://doi.org/10.15439/2015F426

264.

Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. In: Bengio, Y., LeCun, Y. (eds.) 1st International Conference on Learning Representations, ICLR 2013, Scottsdale, Arizona, USA, 2–4 May 2013, Workshop Track Proceedings (2013). http://arxiv.org/abs/1301.3781

265.

Milczek, J.K., Bogucki, R., Lasek, J., Tadeusiak, M.: Early warning system for seismic events in coal mines using machine learning. In: Ganzha, M., Maciaszek, L., Paprzycki, M. (eds.) Proceedings of the 2016 Federated Conference on Computer Science and Information Systems. Annals of Computer Science and Information Systems, vol. 8, pp. 213–220. IEEE (2016). https://doi.org/10.15439/2016F420

266.

Min, F., Hu, Q., Zhu, W.: Feature selection with test cost constraint. Int. J. Approx. Reason. 55(1), 167–179 (2014)MathSciNetMATHCrossRef

267.

Miranda, T., Correia, A.G., e Sousa, L.R.: Bayesian methodology for updating geomechanical parameters and uncertainty quantification. Int. J. Rock Mech. Mining Sci. 46(7), 1144–1153 (2009). https://doi.org/10.1016/j.ijrmms.2009.03.008CrossRef

268.

Moczulski, W., Przystałka, P., Sikora, M., Zimroz, R.: Modern ICT and mechatronic systems in contemporary mining industry. In: Rough Sets - International Joint Conference, IJCRS 2016, Santiago de Chile, Chile, 7–11 October 2016, Proceedings, pp. 33–42 (2016). https://doi.org/10.1007/978-3-319-47160-0_3

269.

Mohamed, M., Oussalah, M.: SRL-ESA-TextSum: a text summarization approach based on semantic role labeling and explicit semantic analysis. Inf. Process. Manag. 56(4), 1356–1372 (2019). https://doi.org/10.1016/j.ipm.2019.04.003CrossRef

270.

Mönks, U., Dörksen, H., Lohweg, V., Hübner, M.: Information fusion of conflicting input data. Sensors 16(11), E1798 (2016)CrossRef

271.

Moore, R.E., Kearfott, R.B., Cloud, M.J.: Introduction to Interval Analysis. Society for Industrial and Applied Mathematics (2009)

272.

Mörchen, F., Ultsch, A.: Optimizing time series discretization for knowledge discovery. In: Proceedings of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery in Data Mining, KDD 2005, pp. 660–665. ACM, New York (2005). https://doi.org/10.1145/1081870.1081953

273.

Moshkov, M.J., Piliszczuk, M., Zielosko, B.: On construction of partial reducts and irreducible partial decision rules. Fund. Inform. 75(1–4), 357–374 (2007)MathSciNetMATH

274.

Mu, L., Ji, Y.: Integrated coal mine safety monitoring system. In: Zhang, W. (ed.) SEKEIE 2012, pp. 365–371. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-29455-6_51CrossRef

275.

Mulargia, F., Stark, P.B., Geller, R.J.: Why is probabilistic seismic hazard analysis (PSHA) still used? Phys. Earth Planet. Inter. 264, 63–75 (2017). https://doi.org/10.1016/j.pepi.2016.12.002CrossRef

276.

Murphy, K.P.: Machine Learning: A Probabilistic Perspective. The MIT Press, Cambridge (2012)MATH

277.

Nasiri, H., Nasehi, S., Goudarzi, M.: Evaluation of distributed stream processing frameworks for IoT applications in Smart Cities. J. Big Data 6(1), 1–24 (2019). https://doi.org/10.1186/s40537-019-0215-2CrossRef

278.

Nguyen, H.S.: Approximate boolean reasoning: foundations and applications in data mining. Trans. Rough Sets 5, 334–506 (2006). https://doi.org/10.1007/11847465_16MathSciNetCrossRefMATH

279.

Nguyen, H.S., Ślęzak, D.: Approximate reducts and association rules. In: Zhong, N., Skowron, A., Ohsuga, S. (eds.) RSFDGrC 1999. LNCS (LNAI), vol. 1711, pp. 137–145. Springer, Heidelberg (1999). https://doi.org/10.1007/978-3-540-48061-7_18CrossRef

280.

Nguyen, S.H., Szczuka, M.: Feature selection in decision systems with constraints. In: Flores, V., Gomide, F., Janusz, A., Meneses, C., Miao, D., Peters, G., Ślęzak, D., Wang, G., Weber, R., Yao, Y. (eds.) IJCRS 2016. LNCS (LNAI), vol. 9920, pp. 537–547. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-47160-0_49CrossRef

281.

Nguyen, S.H., Skowron, A.: Quantization of real value attributes - rough set and boolean reasoning approach. In: Proceedings of the Second Joint Annual Conference on Information Sciences, Wrightsville Beach, North Carolina, 28 September–1 October 1995, pp. 34–37 (1995)

282.

Nguyen, T.T., Skowron, A.: Rough-Granular Computing in Human-Centric Information Processing. In: Bargiela, A., Pedrycz, W. (eds.) Human-Centric Information Processing Through Granular Modelling. Studies in Computational Intelligence, vol. 182, pp. 1–30. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-540-92916-1_1CrossRef

283.

Nixon, M.S., Aguado, A.S.: Feature Extraction and Image Processing for Computer Vision, 4th edn. Academic Press (2020)

284.

Nogueira, S.: Quantifying the stability of feature selection. Ph.D. thesis, University of Manchester (2018)

285.

Nogueira, S., Sechidis, K., Brown, G.: On the stability of feature selection algorithms. J. Mach. Learn. Res. 18, 174:1–174:54 (2017)

286.

Palma-Mendoza, R.-J., Rodriguez, D., de-Marcos, L.: Distributed ReliefF-based feature selection in Spark. Knowl. Inf. Syst. 57(1), 1–20 (2018). https://doi.org/10.1007/s10115-017-1145-yCrossRef

287.

Parmar, N., Ramachandran, P., Vaswani, A., Bello, I., Levskaya, A., Shlens, J.: Stand-alone self-attention in vision models. In: Wallach, H.M., Larochelle, H., Beygelzimer, A., d’Alché-Buc, F., Fox, E.B., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019, 8–14 December 2019, Canada, Vancouver, BC, pp. 68–80 (2019)

288.

Pawlak, Z.: Rough Sets: Theoretical Aspects of Reasoning about Data, System Theory, Knowledge Engineering and Problem Solving, vol. 9. Kluwer (1991)

289.

Pawlak, Z., Skowron, A.: Rough membership functions. In: Advances in the Dempster-Shafer Theory of Evidence, pp. 251–271. Wiley, New York (1994)

290.

Pawlak, Z., Skowron, A.: Rough sets: some extensions. Inf. Sci. 177(1), 28–40 (2007)MathSciNetMATHCrossRef

291.

Pawlak, Z., Skowron, A.: Rudiments of rough sets. Inf. Sci. 177(1), 3–27 (2007)MathSciNetMATHCrossRef

292.

Pawłowski, K., Kurach, K.: Detecting methane outbreaks from time series data with deep neural networks. In: Yao, Y., Hu, Q., Yu, H., Grzymala-Busse, J.W. (eds.) RSFDGrC 2015. LNCS (LNAI), vol. 9437, pp. 475–484. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-25783-9_42CrossRef

293.

Pearl, J.: Causal inference in statistics: an overview. Stat. Surv. 3, 96–146 (2009). https://doi.org/10.1214/09-SS057MathSciNetCrossRefMATH

294.

Pedrycz, W.: Interpretation of clusters in the framework of shadowed sets. Pattern Recogn. Lett. 26(15), 2439–2449 (2005). https://doi.org/10.1016/j.patrec.2005.05.001CrossRef

295.

Pedrycz, W.: Granular Computing: Analysis and Design of Intelligent Systems. CRC Press, Boca Raton (2013)CrossRef

296.

Pedrycz, W.: Granular computing for data analytics: a manifesto of human-centric computing. IEEE CAA J. Autom. Sinica 5(6), 1025–1034 (2018). https://doi.org/10.1109/JAS.2018.7511213MathSciNetCrossRef

297.

Peng, H., Long, F., Ding, C.H.Q.: Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans. Pattern Anal. Mach. Intell. 27(8), 1226–1238 (2005). https://doi.org/10.1109/TPAMI.2005.159CrossRef

298.

Pennington, J., Socher, R., Manning, C.D.: GloVe: global vectors for word representation. In: Moschitti, A., Pang, B., Daelemans, W. (eds.) Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, EMNLP 2014, 25–29 October 2014, Doha, Qatar, A meeting of SIGDAT, a Special Interest Group of the ACL, pp. 1532–1543. ACL (2014). https://doi.org/10.3115/v1/d14-1162

299.

Perez-Benitez, J.A., Padovese, L.R.: A system for classification of time-series data from industrial non-destructive device. Eng. Appl. Artif. Intell. 26(3), 974–983 (2013). https://doi.org/10.1016/j.engappai.2012.09.006CrossRef

300.

Persson, L.E., Samko, N., Wall, P.: Quasi-monotone weight functions and their characteristics and applications. Math. Inequalities Appl. 15, 685–705 (2012). https://doi.org/10.7153/mia-15-61MathSciNetCrossRefMATH

301.

Podlodowski, Ł.: Utilizing an ensemble of SVMs with GMM voting-based mechanism in predicting dangerous seismic events in active coal mines. In: Ganzha, M., Maciaszek, L., Paprzycki, M. (eds.) Proceedings of the 2016 Federated Conference on Computer Science and Information Systems. Annals of Computer Science and Information Systems, vol. 8, pp. 235–238. IEEE (2016). https://doi.org/10.15439/2016F122

302.

Polikar, R., DePasquale, J., Mohammed, H.S., Brown, G., Kuncheva, L.I.: Learn++.MF: a random subspace approach for the missing feature problem. Pattern Recognit. 43(11), 3817–3832 (2010)

303.

Ponciano, V., et al.: Mobile computing technologies for health and mobility assessment: research design and results of the timed up and go test in older adults. Sensors 20(12), 3481 (2020). https://doi.org/10.3390/s20123481CrossRef

304.

Popieul, J.C., Loslever, P., Todoskoff, A., Simon, P., Rotting, M.: Multivariate analysis of human behavior data using fuzzy windowing: example with driver-car-environment system. Eng. Appl. Artif. Intell. 25(5), 989–996 (2012). https://doi.org/10.1016/j.engappai.2011.11.011CrossRef

305.

Potdar, K., Pardawala, T., Pai, C.: A comparative study of categorical variable encoding techniques for neural network classifiers. Int. J. Comput. Appl. 175, 7–9 (2017). https://doi.org/10.5120/ijca2017915495CrossRef

306.

Przystałka, P., Sikora, M. (eds.): Zintegrowany, szkieletowy system wspmagania decyzji dla systemów monitorowania procesów, urządzeń i zagrożeń. Monograficzna Seria Wydawnicza Instyututu Technik Innowacyjnych EMAG (2017)

307.

Qian, J., Miao, D., Zhang, Z., Li, W.: Hybrid approaches to attribute reduction based on indiscernibility and discernibility relation. Int. J. Approx. Reason. 52(2), 212–230 (2011). https://doi.org/10.1016/j.ijar.2010.07.011

308.

Qian, J., et al.: Introducing self-organized maps (SOM) as a visualization tool for materials research and education. Results Mater. 4, 100020 (2019). https://doi.org/10.1016/j.rinma.2019.100020

309.

Qian, J., Dang, C., Yue, X., Zhang, N.: Attribute reduction for sequential three-way decisions under dynamic granulation. Int. J. Approx. Reason. 85, 196–216 (2017). https://doi.org/10.1016/j.ijar.2017.03.009MathSciNetCrossRefMATH

310.

Qian, J., Lv, P., Yue, X., Liu, C., Jing, Z.: Hierarchical attribute reduction algorithms for big data using MapReduce. Knowl.-Based Syst. 73, 18–31 (2015)CrossRef

311.

Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers Inc., San Francisco (1993)

312.

Radford, A., Metz, L., Chintala, S.: Unsupervised representation learning with deep convolutional generative adversarial networks. In: Bengio, Y., LeCun, Y. (eds.) 4th International Conference on Learning Representations, ICLR 2016, San Juan, Puerto Rico, 2–4 May 2016, Conference Track Proceedings (2016)

313.

Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I.: Language models are unsupervised multitask learners. OpenAI Blog (2019)

314.

Raffel, C., et al.: Exploring the limits of transfer learning with a unified text-to-text transformer. J. Mach. Learn. Res. 21, 140:1–140:67 (2020)

315.

Rakthanmanon, T., et al.: Addressing big data time series: mining trillions of time series subsequences under dynamic time warping. ACM Trans. Knowl. Discov. Data 7(3), 10:1–10:31 (2013). https://doi.org/10.1145/2500489

316.

Ramírez-Gallego, S., et al.: Fast-mRMR: fast minimum redundancy maximum relevance algorithm for high-dimensional big data. Int. J. Intell. Syst. 32, 134–152 (2017)CrossRef

317.

Ran, Y., Shi, Y., Zhang, Z.: Local ratio method on partial set multi-cover. J. Comb. Optim. 34(1), 302–313 (2017)MathSciNetMATHCrossRef

318.

Ratner, A., Hancock, B., Dunnmon, J., Sala, F., Pandey, S., Ré, C.: Training complex models with multi-task weak supervision. In: AAAI 2019, Honolulu, Hawaii, USA, 27 January–1 February 2019, pp. 4763–4771. AAAI Press (2019). https://doi.org/10.1609/aaai.v33i01.33014763

319.

Read, J., Pfahringer, B., Holmes, G., Frank, E.: Classifier chains: a review and perspectives. CoRR abs/1912.13405 (2019). http://arxiv.org/abs/1912.13405

320.

Read, J., Puurula, A., Bifet, A.: Multi-label classification with meta-labels. In: Kumar, R., Toivonen, H., Pei, J., Huang, J.Z., Wu, X. (eds.) 2014 IEEE International Conference on Data Mining, ICDM 2014, Shenzhen, China, 14–17 December 2014, pp. 941–946. IEEE Computer Society (2014). https://doi.org/10.1109/ICDM.2014.38

321.

Rehman, M.H., Chang, V., Batool, A., Wah, T.Y.: Big data reduction framework for value creation in sustainable enterprises. Int. J. Inf. Manag. 36(6), 917–928 (2016)CrossRef

322.

dos Reis, D.M., Flach, P.A., Matwin, S., Batista, G.E.A.P.A.: Fast unsupervised online drift detection using incremental kolmogorov-smirnov test. In: Krishnapuram, B., Shah, M., Smola, A.J., Aggarwal, C.C., Shen, D., Rastogi, R. (eds.) Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA, 13–17 August 2016, pp. 1545–1554. ACM (2016). https://doi.org/10.1145/2939672.2939836

323.

Riza, L.S., et al.: Implementing algorithms of rough set theory and fuzzy rough set theory in the R package ‘RoughSets’. Inf. Sci. 287, 68–89 (2014)CrossRef

324.

Röger, H., Mayer, R.: A comprehensive survey on parallelization and elasticity in stream processing. ACM Comput. Surv. 52(2), 1–37 (2019). https://doi.org/10.1145/3303849CrossRef

325.

Rosen, J., et al.: Iterative MapReduce for Large Scale Machine Learning. CoRR abs/1303.3517 (2013)

326.

Roy, A., Pal, S.K.: Fuzzy discretization of feature space for a rough set classifier. Pattern Recogn. Lett. 24(6), 895–902 (2003). https://doi.org/10.1016/S0167-8655(02)00201-5CrossRefMATH

327.

Roy, D., Murty, K.S.R., Mohan, C.K.: Feature selection using deep neural networks. In: Proceedings of IJCNN 2015, pp. 1–6 (2015)

328.

Ruder, S.: An overview of multi-task learning in deep neural networks. CoRR abs/1706.05098 (2017). http://arxiv.org/abs/1706.05098

329.

Rudin, C.: Please stop explaining black box models for high stakes decisions. CoRR abs/1811.10154 (2018). http://arxiv.org/abs/1811.10154

330.

Menasalvas Ruiz, E., et al.: Profiling lung cancer patients using electronic health records. J. Med. Syst. 42(7), 1–10 (2018). https://doi.org/10.1007/s10916-018-0975-9CrossRef

331.

Ruta, D., Cen, L.: Self-organized predictor of methane concentration warnings in coal mines. In: Yao, Y., Hu, Q., Yu, H., Grzymala-Busse, J.W. (eds.) RSFDGrC 2015. LNCS (LNAI), vol. 9437, pp. 485–493. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-25783-9_43CrossRef

332.

Rzeszótko, J., Nguyen, S.H.: Machine learning for traffic prediction. Fund. Inform. 119(3–4), 407–420 (2012)MathSciNet

333.

Saeys, Y., Abeel, T., Van de Peer, Y.: Robust feature selection using ensemble feature selection techniques. In: Daelemans, W., Goethals, B., Morik, K. (eds.) ECML PKDD 2008. LNCS (LNAI), vol. 5212, pp. 313–325. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-87481-2_21CrossRef

334.

Salaken, S.M., Khosravi, A., Nguyen, T., Nahavandi, S.: Seeded transfer learning for regression problems with deep learning. Expert Syst. Appl. 115, 565–577 (2019). https://doi.org/10.1016/j.eswa.2018.08.041

335.

Sarawagi, S., Thomas, S., Agrawal, R.: Integrating association rule mining with relational database systems: alternatives and implications. Data Min. Knowl. Disc. 4(2–3), 89–125 (2000)CrossRef

336.

Schaefer, M., Eikermann, M.: Contact-free respiratory monitoring using bed-wheel sensors: a valid respiratory monitoring technique with significant potential impact on public health. J. Appl. Physiol. 126, 1430–1431 (2019). https://doi.org/10.1152/japplphysiol.00198.2019CrossRef

337.

Scovanner, P., Ali, S., Shah, M.: A 3-dimensional sift descriptor and its application to action recognition. In: Proceedings of the 15th ACM International Conference on Multimedia, MM 2007, pp. 357–360. ACM, New York (2007). https://doi.org/10.1145/1291233.1291311

338.

Senawi, A., Wei, H., Billings, S.A.: A new maximum relevance-minimum multicollinearity (MRmMC) method for feature selection and ranking. Pattern Recogn. 67, 47–61 (2017)CrossRef

339.

Sesma-Sara, M., Mesiar, R., Bustince, H.: Weak and directional monotonicity of functions on Riesz spaces to fuse uncertain data. Fuzzy Sets Syst. 386, 145–160 (2020). https://doi.org/10.1016/j.fss.2019.01.019MathSciNetCrossRefMATH

340.

Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: time series to image conversion approach. Appl. Soft Comput. 70, 525–538 (2018). https://doi.org/10.1016/j.asoc.2018.04.024CrossRef

341.

Shah, D., Isah, H., Zulkernine, F.: Stock market analysis: a review and taxonomy of prediction techniques. Int. J. Financ. Stud. 7(2), 26 (2019). https://doi.org/10.3390/ijfs7020026CrossRef

342.

Shah, J.S.: Novel statistical approaches for missing values in truncated high-dimensional metabolomics data with a detection threshold. Ph.D. thesis, University of Louisville (2017)

343.

Shawi, R.E., Sakr, S., Talia, D., Trunfio, P.: Big data systems meet machine learning challenges: towards big data science as a service. Big Data Res. 14, 1–11 (2018). https://doi.org/10.1016/j.bdr.2018.04.004CrossRef

344.

She, Y.H., Qian, Z.H., He, X.L., Wang, J.T., Qian, T., Zheng, W.L.: On generalization reducts in multi-scale decision tables. Inf. Sci. 555, 104–124 (2021). https://doi.org/10.1016/j.ins.2020.12.045MathSciNetCrossRefMATH

345.

Sheikhpour, R., Sarram, M.A., Gharaghani, S., Chahooki, M.A.Z.: A survey on semi-supervised feature selection methods. Pattern Recognit. 64, 141–158 (2017). https://doi.org/10.1016/j.patcog.2016.11.003CrossRefMATH

346.

Sheikhpour, R., Sarram, M.A., Gharaghani, S., Chahooki, M.A.Z.: A robust graph-based semi-supervised sparse feature selection method. Inf. Sci. 531, 13–30 (2020). https://doi.org/10.1016/j.ins.2020.03.094MathSciNetCrossRefMATH

347.

Shishavan, S.A.S., Gündogdu, F.K., Farrokhizadeh, E., Donyatalab, Y., Kahraman, C.: Novel similarity measures in spherical fuzzy environment and their applications. Eng. Appl. Artif. Intell. 94, 103837 (2020). https://doi.org/10.1016/j.engappai.2020.103837CrossRefMATH

348.

Sikder, I.U., Munakata, T.: Application of rough set and decision tree for characterization of premonitory factors of low seismic activity. Expert Syst. Appl. 36(1), 102–110 (2009). https://doi.org/10.1016/j.eswa.2007.09.032CrossRef

349.

Sikora, M., Sikora, B.: Improving prediction models applied in systems monitoring natural hazards and machinery. Int. J. Appl. Math. Comput. Sci. 22(2), 477–491 (2012). https://doi.org/10.2478/v10006-012-0036-3CrossRefMATH

350.

Singh, P., Dhiman, G.: A hybrid fuzzy time series forecasting model based on granular computing and bio-inspired optimization approaches. J. Comput. Sci. 27, 370–385 (2018). https://doi.org/10.1016/j.jocs.2018.05.008CrossRef

351.

Skowron, A., Dutta, S.: Rough sets: past, present, and future. Nat. Comput. 17(4), 855–876 (2018). https://doi.org/10.1007/s11047-018-9700-3MathSciNetCrossRef

352.

Skowron, A., Jankowski, A., Dutta, S.: Interactive granular computing. Granular Comput. 1(2), 95–113 (2015). https://doi.org/10.1007/s41066-015-0002-1CrossRefMATH

353.

Skowron, A., Rauszer, C.: The discernibility matrices and functions in information systems. In: Słowiński, R. (ed.) Intelligent Decision Support. Theory and Decision Library, vol. 11, pp. 331–362. Springer, Dordrecht (1992). https://doi.org/10.1007/978-94-015-7975-9_21CrossRef

354.

Skowron, A., Wasilewski, P.: Interactive information systems: toward perception based computing. Theor. Comput. Sci. 454, 240–260 (2012). https://doi.org/10.1016/j.tcs.2012.04.019MathSciNetCrossRefMATH

355.

Ślęzak, D.: Normalized decision functions and measures for inconsistent decision tables analysis. Fund. Inform. 44(3), 291–319 (2000)MathSciNetMATH

356.

Ślęzak, D.: Approximate entropy reducts. Fund. Inform. 53(3–4), 365–390 (2002)MathSciNetMATH

357.

Ślęzak, D.: Rough sets and functional dependencies in data: foundations of association reducts. Trans. Comput. Sci. 5, 182–205 (2009)MATH

358.

Ślęzak, D.: Compound analytics of compound data within RDBMS framework – Infobright’s perspective. In: Kim, T., Lee, Y., Kang, B.-H., Ślęzak, D. (eds.) FGIT 2010. LNCS, vol. 6485, pp. 39–40. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-17569-5_5CrossRef

359.

Ślęzak, D., Glick, R., Betliński, P., Synak, P.: A new approximate query engine based on intelligent capture and fast transformations of granulated data summaries. J. Intell. Inf. Syst. 50(2), 385–414 (2017). https://doi.org/10.1007/s10844-017-0471-6CrossRef

360.

Ślęzak, D., et al.: A framework for learning and embedding multi-sensor forecasting models into a decision support system: a case study of methane concentration in coal mines. Inf. Sci. 451–452, 112–133 (2018)MathSciNetCrossRef

361.

Ślęzak, D., Grzegorowski, M., Janusz, A., Stawicki, S.: Interactive Data Exploration with Infolattices. Abstract Materials of BAFI 2015 (2015)

362.

Ślęzak, D., Grzegorowski, M., Janusz, A., Stawicki, S.: Toward interactive attribute selection with infolattices – a position paper. In: Polkowski, L., et al. (eds.) IJCRS 2017. LNCS (LNAI), vol. 10314, pp. 526–539. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-60840-2_38CrossRef

363.

Ślęzak, D., Janusz, A.: Ensembles of bireducts: towards robust classification and simple representation. In: Kim, T., et al. (eds.) FGIT 2011. LNCS, vol. 7105, pp. 64–77. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-27142-7_9CrossRef

364.

Ślęzak, D., Stawicki, S.: The problem of finding the simplest classifier ensemble is NP-hard – a rough-set-inspired formulation based on decision bireducts. In: Bello, R., Miao, D., Falcon, R., Nakata, M., Rosete, A., Ciucci, D. (eds.) IJCRS 2020. LNCS (LNAI), vol. 12179, pp. 204–212. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-52705-1_15CrossRef

365.

Ślęzak, D., Widz, S.: Evolutionary inspired optimization of feature subset ensembles. In: Takagi, H., Abraham, A., Köppen, M., Yoshida, K., de Carvalho, A.C.P.L.F. (eds.) Second World Congress on Nature & Biologically Inspired Computing, NaBIC 2010, 15–17 December 2010, Kitakyushu, Japan, pp. 437–442. IEEE (2010). https://doi.org/10.1109/NABIC.2010.5716365

366.

Smuk, M.: Missing data methodology: sensitivity analysis after multiple imputation. Ph.D. thesis, University of London (2015)

367.

Sobhani, P., Viktor, H., Matwin, S.: Learning from imbalanced data using ensemble methods and cluster-based undersampling. In: Appice, A., Ceci, M., Loglisci, C., Manco, G., Masciari, E., Ras, Z.W. (eds.) NFMCP 2014. LNCS (LNAI), vol. 8983, pp. 69–83. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-17876-9_5CrossRef

368.

Son, L.H.: Dealing with the new user cold-start problem in recommender systems: a comparative review. Inf. Syst. 58, 87–104 (2016). https://doi.org/10.1016/j.is.2014.10.001CrossRef

369.

Sorzano, C.O.S., Vargas, J., Montano, A.P.: A survey of dimensionality reduction techniques (2014)

370.

de Souto, M.C.P., Costa, I.G., de Araujo, D.S.A., Ludermir, T.B., Schliep, A.: Clustering cancer gene expression data: a comparative study. BMC Bioinform. 9, 1–14 (2008)CrossRef

371.

Sripada, S.G., Reiter, E., Hunter, J., Yu, J., Davy, I.P.: Modelling the task of summarising time series data using ka techniques. In: Macintosh, A., Moulton, M., Preece, A. (eds.) Applications and Innovations in Intelligent Systems IX, pp. 183–196. Springer, London (2002). https://doi.org/10.1007/978-1-4471-0149-9_14CrossRef

372.

Stańczyk, U., Zielosko, B., Jain, L.C.: Advances in feature selection for data and pattern recognition: an introduction. In: Stańczyk, U., Zielosko, B., Jain, L.C. (eds.) Advances in Feature Selection for Data and Pattern Recognition. ISRL, vol. 138, pp. 1–9. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-67588-6_1CrossRefMATH

373.

Stawicki, S., Ślęzak, D., Janusz, A., Widz, S.: Decision bireducts and decision reducts - a comparison. Int. J. Approx. Reason. 84, 75–109 (2017)MathSciNetMATHCrossRef

374.

Stoppiglia, H., Dreyfus, G., Dubois, R., Oussar, Y.: Ranking a random feature for variable and feature selection. J. Mach. Learn. Res. 3, 1399–1414 (2003). http://jmlr.org/papers/v3/stoppiglia03a.html

375.

Sutskever, I., Martens, J., Dahl, G., Hinton, G.: On the importance of initialization and momentum in deep learning. In: Proceedings of the 30th International Conference on International Conference on Machine Learning, ICML 2013, vol. 28. pp. 1139–1147. JMLR.org (2013). http://dl.acm.org/citation.cfm?id=3042817.3043064

376.

Świniarski, R.W., Skowron, A.: Rough set methods in feature selection and recognition. Pattern Recogn. Lett. 24(6), 833–849 (2003)MATHCrossRef

377.

Szczuka, M.S., Ślęzak, D.: How deep data becomes big data. In: Proceedings of IFSA/NAFIPS 2013, pp. 579–584 (2013)

378.

Szczuka, M.S., Wojdyłło, P.: Neuro-wavelet classifiers for EEG signals based on rough set methods. Neurocomputing 36(1–4), 103–122 (2001)MATHCrossRef

379.

Sze, V., Chen, Y., Yang, T., Emer, J.S.: Efficient processing of deep neural networks: a tutorial and survey. Proc. IEEE 105(12), 2295–2329 (2017). https://doi.org/10.1109/JPROC.2017.2761740CrossRef

380.

Taguchi, Y.H.: Unsupervised Feature Extraction Applied to Bioinformatics. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-22456-1CrossRefMATH

381.

Teixeira de Souza, J., Matwin, S., Japkowicz, N.: Parallelizing feature selection. Algorithmica 45(3), 433–456 (2006)

382.

Tenney, I., Das, D., Pavlick, E.: BERT rediscovers the classical NLP pipeline. In: Korhonen, A., Traum, D.R., Màrquez, L. (eds.) Proceedings of the 57th Conference of the Association for Computational Linguistics, ACL 2019, Florence, Italy, 28July–2 August 2019, Volume 1: Long Papers, pp. 4593–4601. Association for Computational Linguistics (2019). https://doi.org/10.18653/v1/p19-1452

383.

Tran, T.N., Afanador, N.L., Buydens, L.M., Blanchet, L.: Interpretation of variable importance in partial least squares with significance multivariate correlation (SMC). Chemom. Intell. Lab. Syst. 138, 153–160 (2014)CrossRef

384.

Triguero, I., Peralta, D., Bacardit, J., García, S., Herrera, F.: MRPR: a MapReduce solution for prototype reduction in big data classification. Neurocomputing 150, 331–345 (2015)CrossRef

385.

Tsai, C.F., Chen, Y.C.: The optimal combination of feature selection and data discretization: an empirical study. Inf. Sci. 505, 282–293 (2019). https://doi.org/10.1016/j.ins.2019.07.091CrossRef

386.

Tsakiridis, N.L., et al.: Versatile internet of things for agriculture: an explainable AI approach. In: Maglogiannis, I., Iliadis, L., Pimenidis, E. (eds.) AIAI 2020. IAICT, vol. 584, pp. 180–191. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-49186-4_16CrossRef

387.

Urbanowicz, R.J., Meeker, M., Cava, W.G.L., Olson, R.S., Moore, J.H.: Relief-based feature selection: introduction and review. J. Biomed. Inform. 85, 189–203 (2018). https://doi.org/10.1016/j.jbi.2018.07.014CrossRef

388.

Van Der Maaten, L., Postma, E., Van den Herik, J.: Dimensionality reduction: a comparative review. Tilburg University Technical Report, TiCC-TR 2009 (2009)

389.

Vaswani, A., et al.: Attention is all you need. In: Guyon, I., et al. (eds.) Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 4–9 December 2017, Long Beach, CA, USA, pp. 5998–6008 (2017)

390.

Vergara, J.R., Estévez, P.A.: A review of feature selection methods based on mutual information. Neural Comput. Appl. 24(1), 175–186 (2013). https://doi.org/10.1007/s00521-013-1368-0CrossRef

391.

Vincent, P., Larochelle, H., Bengio, Y., Manzagol, P.A.: Extracting and composing robust features with denoising autoencoders. In: Proceedings of the 25th International Conference on Machine Learning, ICML 2008, pp. 1096–1103. ACM, New York (2008). https://doi.org/10.1145/1390156.1390294. http://doi.acm.org/10.1145/1390156.1390294

392.

Špirková, J., Beliakov, G., Bustince, H., Fernandez, J.: Mixture functions and their monotonicity. Inf. Sci. 481, 520–549 (2019). https://doi.org/10.1016/j.ins.2018.12.090. http://www.sciencedirect.com/science/article/pii/S002002551831048X

393.

Wachla, D., Moczulski, W.A.: Identification of dynamic diagnostic models with the use of methodology of knowledge discovery in databases. Eng. Appl. Artif. Intell. 20(5), 699–707 (2007). https://doi.org/10.1016/j.engappai.2006.11.002CrossRef

394.

Wang, H., Xu, Z., Fujita, H., Liu, S.: Towards felicitous decision making: an overview on challenges and trends of big data. Inf. Sci. 367–368, 747–765 (2016). https://doi.org/10.1016/j.ins.2016.07.007CrossRef

395.

Wang, H., Kläser, A., Schmid, C., Liu, C.L.: Dense trajectories and motion boundary descriptors for action recognition. Int. J. Comput. Vision 103(1), 60–79 (2013). https://doi.org/10.1007/s11263-012-0594-8MathSciNetCrossRef

396.

Wang, H., Bah, M.J., Hammad, M.: Progress in outlier detection techniques: a survey. IEEE Access 7, 107964–108000 (2019). https://doi.org/10.1109/ACCESS.2019.2932769CrossRef

397.

Wang, L., Lin, Z.Q., Wong, A.: COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images. Sci. Rep. 10, 1–12 (2020). https://doi.org/10.1038/s41598-020-76550-zCrossRef

398.

Wang, L., Wang, Y., Chang, Q.: Feature selection methods for big data bioinformatics: a survey from the search perspective. Methods 111, 21–31 (2016). https://doi.org/10.1016/j.ymeth.2016.08.014CrossRef

399.

Wang, X., Liu, X., Japkowicz, N., Matwin, S.: Resampling and cost-sensitive methods for imbalanced multi-instance learning. In: 2013 IEEE 13th International Conference on Data Mining Workshops, pp. 808–816 (2013)

400.

Widz, S., Ślęzak, D.: Granular attribute selection: a case study of rough set approach to MRI segmentation. In: Proceedings of PReMI 2013, pp. 47–52 (2013)

401.

Wieczorkowska, A., Wróblewski, J., Synak, P., Ślęzak, D.: Application of temporal descriptors to musical instrument sound recognition. J. Intell. Inf. Syst. 21(1), 71–93 (2003)MATHCrossRef

402.

Wójtowicz, A.: Ensemble classification of incomplete data - a non-imputation approach with an application in ovarian tumour diagnosis support. Ph.D. thesis, University in Poznań (2017)

403.

Wójtowicz, A., Żywica, P., Stachowiak, A., Dyczkowski, K.: Solving the problem of incomplete data in medical diagnosis via interval modeling. Appl. Soft Comput. 47, 424–437 (2016). https://doi.org/10.1016/j.asoc.2016.05.029CrossRef

404.

Wróblewski, J.: Ensembles of classifiers based on approximate reducts. Fund. Inform. 47(3–4), 351–360 (2001)MathSciNetMATH

405.

Wróblewski, J., Stawicki, S.: SQL-based KDD with infobright’s RDBMS: attributes, reducts, trees. In: Kryszkiewicz, M., Cornelis, C., Ciucci, D., Medina-Moreno, J., Motoda, H., Raś, Z.W. (eds.) RSEISP 2014. LNCS (LNAI), vol. 8537, pp. 28–41. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-08729-0_3CrossRef

406.

Wu, C., Buyya, R., Ramamohanarao, K.: Cloud pricing models: taxonomy, survey, and interdisciplinary challenges. ACM Comput. Surv. 52(6), 108:1–108:36 (2020). https://doi.org/10.1145/3342103

407.

Wu, X., Pellegrini, F.D., Gao, G., Casale, G.: A framework for allocating server time to spot and on-demand services in cloud computing. TOMPECS 4(4), 20:1–20:31 (2019). https://doi.org/10.1145/3366682

408.

Wu, X., et al.: Top 10 algorithms in data mining. Knowl. Inf. Syst. 14(1), 1–37 (2007). https://doi.org/10.1007/s10115-007-0114-2CrossRef

409.

Wu, Z., Pan, S., Long, G., Jiang, J., Chang, X., Zhang, C.: Connecting the dots: multivariate time series forecasting with graph neural networks. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD 2020, pp. 753–763. Association for Computing Machinery, New York (2020). https://doi.org/10.1145/3394486.3403118

410.

Xie, J., Wu, J., Qian, Q.: Feature selection algorithm based on association rules mining method. In: Proceedings of ICIS 2009, pp. 357–362 (2009)

411.

Xioufis, E.S., Spiliopoulou, M., Tsoumakas, G., Vlahavas, I.: Dealing with concept drift and class imbalance in multi-label stream classification. In: Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence - Volume Two, IJCAI 2011, pp. 1583–1588. AAAI Press (2011). https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-266

412.

Spyromitros-Xioufis, E., Tsoumakas, G., Groves, W., Vlahavas, I.: Multi-target regression via input space expansion: treating targets as inputs. Mach. Learn. 104(1), 55–98 (2016). https://doi.org/10.1007/s10994-016-5546-zMathSciNetCrossRefMATH

413.

Xue, B., Zhang, M., Browne, W.N., Yao, X.: A survey on evolutionary computation approaches to feature selection. IEEE Trans. Evol. Comput. 20(4), 606–626 (2016). https://doi.org/10.1109/TEVC.2015.2504420CrossRef

414.

Yang, H., Lin, H., Ding, K.: Sliding window denoising k-singular value decomposition and its application on rolling bearing impact fault diagnosis. J. Sound Vib. 421, 205–219 (2018). https://doi.org/10.1016/j.jsv.2018.01.051CrossRef

415.

Yang, Y., Gopal, S.: Multilabel classification with meta-level features in a learning-to-rank framework. Mach. Learn. 88(1–2), 47–68 (2012). https://doi.org/10.1007/s10994-011-5270-7MathSciNetCrossRefMATH

416.

Yang, Y., Webb, G.I.: Discretization for Naive-Bayes learning: managing discretization bias and variance. Mach. Learn. 74(1), 39–74 (2009). https://doi.org/10.1007/s10994-008-5083-5CrossRefMATH

417.

Yang, Y., Webb, G.I., Wu, X.: Discretization methods. In: Maimon, O., Rokach, L. (eds.) The Data Mining and Knowledge Discovery Handbook, pp. 113–130. Springer, Boston (2005). https://doi.org/10.1007/0-387-25465-X_6CrossRef

418.

Yao, Y.: Three-way decision and granular computing. Int. J. Approx. Reason. 103, 107–123 (2018). https://doi.org/10.1016/j.ijar.2018.09.005CrossRefMATH

419.

Yao, Y., Zhao, Y., Wang, J.: On reduct construction algorithms. Trans. Comput. Sci. 2, 100–117 (2008)MATH

420.

Yao, Y., Zhong, N.: Granular computing. In: Wah, B.W. (ed.) Wiley Encyclopedia of Computer Science and Engineering. Wiley, Hoboken (2008)

421.

Yin, J., Zhao, W.: Fault diagnosis network design for vehicle on-board equipments of high-speed railway: a deep learning approach. Eng. Appl. Artif. Intell. 56, 250–259 (2016). https://doi.org/10.1016/j.engappai.2016.10.002CrossRef

422.

Zadeh, L.A.: Fuzzy sets. Inf. Control 8(3), 338–353 (1965). https://doi.org/10.1016/S0019-9958(65)90241-XCrossRefMATH

423.

Zadeh, L.A.: Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic. Fuzzy Sets Syst. 90(2), 111–127 (1997)MathSciNetMATHCrossRef

424.

Zadeh, L.A.: From computing with numbers to computing with words—from manipulation of measurements to manipulation of perceptions. In: Azvine, B., Nauck, D.D., Azarmi, N. (eds.) Intelligent Systems and Soft Computing. LNCS (LNAI), vol. 1804, pp. 3–40. Springer, Heidelberg (2000). https://doi.org/10.1007/10720181_1CrossRef

425.

Zagorecki, A.: Prediction of methane outbreaks in coal mines from multivariate time series using random forest. In: Yao, Y., Hu, Q., Yu, H., Grzymala-Busse, J.W. (eds.) RSFDGrC 2015. LNCS (LNAI), vol. 9437, pp. 494–500. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-25783-9_44CrossRef

426.

Zagorecki, A.: A versatile approach to classification of multivariate time series data. In: Ganzha, M., Maciaszek, L.A., Paprzycki, M. (eds.) 2015 Federated Conference on Computer Science and Information Systems, FedCSIS 2015, Lódz, Poland, 13–16 September 2015, pp. 407–410. IEEE (2015)

427.

Zdravevski, E., Lameski, P., Dimitrievski, A., Grzegorowski, M., Apanowicz, C.: Cluster-size optimization within a cloud-based ETL framework for Big Data. In: 2019 IEEE International Conference on Big Data (Big Data), Los Angeles, CA, USA, 9–12 December 2019, pp. 3754–3763. IEEE (2019). https://doi.org/10.1109/BigData47090.2019.9006547

428.

Zdravevski, E., Lameski, P., Kulakov, A.: Automatic feature engineering for prediction of dangerous seismic activities in coal mines. In: Ganzha, M., Maciaszek, L., Paprzycki, M. (eds.) Proceedings of the 2016 Federated Conference on Computer Science and Information Systems. Annals of Computer Science and Information Systems, vol. 8, pp. 245–248. IEEE (2016). https://doi.org/10.15439/2016F152

429.

Zdravevski, E., Lameski, P., Mingov, R., Kulakov, A., Gjorgjevikj, D.: Robust histogram-based feature engineering of time series data. In: Proceedings of FedCSIS 2015, pp. 381–388 (2015)

430.

Zdravevski, E., et al.: Improving activity recognition accuracy in ambient-assisted living systems by automated feature engineering. IEEE Access 5, 5262–5280 (2017). https://doi.org/10.1109/ACCESS.2017.2684913CrossRef

431.

Zhang, S., Zhang, C., Yang, Q.: Data preparation for data mining. Appl. Artif. Intell. 17(5–6), 375–381 (2003). https://doi.org/10.1080/713827180CrossRef

432.

Zhang, X., Qian, B., Cao, S., Li, Y., Chen, H., Zheng, Y., Davidson, I.: Inprem: an interpretable and trustworthy predictive model for healthcare. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD 2020, pp. 450–460. Association for Computing Machinery, New York (2020). https://doi.org/10.1145/3394486.3403087

433.

Zhang, Y., Miao, D., Pedrycz, W., Zhao, T., Xu, J., Yu, Y.: Granular structure-based incremental updating for multi-label classification. Knowl. Based Syst. 189, 105066 (2020). https://doi.org/10.1016/j.knosys.2019.105066CrossRef

434.

Zhao, X.R., Yao, Y.: Three-way fuzzy partitions defined by shadowed sets. Inf. Sci. 497, 23–37 (2019). https://doi.org/10.1016/j.ins.2019.05.022CrossRefMATH

435.

Zhao, Y., Udell, M.: Missing value imputation for mixed data via gaussian copula. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD 2020, pp. 636–646. Association for Computing Machinery, New York (2020). https://doi.org/10.1145/3394486.3403106

436.

Zhao, Z., Zhang, R., Cox, J., Duling, D., Sarle, W.: Massively parallel feature selection: an approach based on variance preservation. Mach. Learn. 92(1), 195–220 (2013)MathSciNetMATHCrossRef

437.

Zheng, W., Zhu, X., Wen, G., Zhu, Y., Yu, H., Gan, J.: Unsupervised feature selection by self-paced learning regularization. Pattern Recogn. Lett. 132, 4–11 (2020). https://doi.org/10.1016/j.patrec.2018.06.029CrossRef

438.

Zhu, P., Zhu, W., Hu, Q., Zhang, C., Zuo, W.: Subspace clustering guided unsupervised feature selection. Pattern Recogn. 66, 364–374 (2017). https://doi.org/10.1016/j.patcog.2017.01.016CrossRef

439.

Zong, W., Chow, Y., Susilo, W.: Interactive three-dimensional visualization of network intrusion detection data for machine learning. Future Gener. Comput. Syst. 102, 292–306 (2020). https://doi.org/10.1016/j.future.2019.07.045CrossRef

Titel: Selected Aspects of Interactive Feature Extraction
verfasst von: Marek Grzegorowski
Verlag: Springer Berlin Heidelberg
Buch: Transactions on Rough Sets XXIII
Print ISBN: 978-3-662-66543-5

Electronic ISBN: 978-3-662-66544-2

Copyright-Jahr: 2022
DOI: https://doi.org/10.1007/978-3-662-66544-2_8

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"