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The Born and Growing of Chinese Rough Set Community with Help of Professor Zdzisław Pawlak Kapitel lesen Erstes Kapitel lesen

2017 | OriginalPaper | Buchkapitel

Rough Sets in Machine Learning: A Review

verfasst von : Rafael Bello, Rafael Falcon

Erschienen in: Thriving Rough Sets

Verlag: Springer International Publishing

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Abstract

This chapter emphasizes on the role played by rough set theory (RST) within the broad field of Machine Learning (ML). As a sound data analysis and knowledge discovery paradigm, RST has much to offer to the ML community. We surveyed the existing literature and reported on the most relevant RST theoretical developments and applications in this area. The review starts with RST in the context of data preprocessing (discretization, feature selection, instance selection and meta-learning) as well as the generation of both descriptive and predictive knowledge via decision rule induction, association rule mining and clustering. Afterward, we examined several special ML scenarios in which RST has been recently introduced, such as imbalanced classification, multi-label classification, dynamic/incremental learning, Big Data analysis and cost-sensitive learning.

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Vorheriges Kapitel Rough Sets, Rough Mereology and Uncertainty
Nächstes Kapitel Application of Tolerance Rough Sets in Structured and Unstructured Text Categorization: A Survey
Literatur
1.
Abraham, A., Falcon, R., Bello, R.: Rough Set Theory: A True Landmark in Data Analysis. Springer, Berlin, Germany (2009)MATHCrossRef
2.
Bai, H., Ge, Y., Wang, J., Li, D., Liao, Y., Zheng, X.: A method for extracting rules from spatial data based on rough fuzzy sets. Knowl. Based Syst. 57, 28–40 (2014)CrossRef
3.
Bal, M.: Rough sets theory as symbolic data mining method: an application on complete decision table. Inf. Sci. Lett. 2(1), 111–116 (2013)CrossRef
4.
Bang, W.C., Bien, Z.: New incremental learning algorithm in the framework of rough set theory. Int. J. Fuzzy Syst. 1, 25–36 (1999)MathSciNet
5.
Bazan, J.G.: A comparison of dynamic and non-dynamic rough set methods for extracting laws from decision tables. Rough Sets Knowl Discovery 1, 321–365 (1998)MATH
6.
Bazan, J.G., Nguyen, H.S., Nguyen, S.H., Synak, P., Wróblewski, J.: Rough set algorithms in classification problem. In: Rough Set Methods and Applications, pp. 49–88. Springer (2000)
7.
Bello, R., Falcon, R., Pedrycz, W., Kacprzyk, J.: Granular Computing: At the Junction of Rough Sets and Fuzzy Sets. Springer, Berlin, Germany (2008)MATHCrossRef
8.
Bello, R., Gómez, Y., Caballero, Y., Nowe, A., Falcon, R.: Rough sets and evolutionary computation to solve the feature selection problem. In: Abraham, A., Falcon, R., Bello, R. (eds.) Rough Set Theory: A True Landmark in Data Analysis. Studies in Computational Intelligence, vol. 174, pp. 235–260. Springer, Berlin (2009)CrossRef
9.
Bello, R., Nowe, A., Gómez, Y., Caballero, Y.: Using ACO and rough set theory to feature selection. WSEAS Trans. Inf. Sci. Appl. 2(5), 512–517 (2005)
10.
Bello, R., Puris, A., Falcon, R., Gómez, Y.: Feature selection through dynamic mesh optimization. In: Ruiz-Shulcloper, J., Kropatsch, W. (eds.) Progress in Pattern Recognition, Image Analysis and Applications. Lecture Notes in Computer Science, vol. 5197, pp. 348–355. Springer, Berlin (2008)CrossRef
11.
Bello, R., Puris, A., Nowe, A., Martínez, Y., García, M.M.: Two step ant colony system to solve the feature selection problem. In: Iberoamerican Congress on Pattern Recognition, pp. 588–596. Springer (2006)
12.
13.
Bhatt, R.B., Gopal, M.: On fuzzy-rough sets approach to feature selection. Pattern Recogn. Lett. 26(7), 965–975 (2005)CrossRef
14.
Błaszczyński, J., Słowiński, R., Szelkag, M.: Sequential covering rule induction algorithm for variable consistency rough set approaches. Inf. Sci. 181(5), 987–1002 (2011)MathSciNetCrossRef
15.
Caballero, Y., Bello, R., Alvarez, D., Garcia, M.M.: Two new feature selection algorithms with rough sets theory. In: IFIP International Conference on Artificial Intelligence in Theory and Practice, pp. 209–216. Springer (2006)
16.
Caballero, Y., Bello, R., Alvarez, D., Gareia, M.M., Pizano, Y.: Improving the k-nn method: rough set in edit training set. In: Professional Practice in Artificial Intelligence, pp. 21–30. Springer (2006)
17.
Caballero, Y., Bello, R., Arco, L., García, M., Ramentol, E.: Knowledge discovery using rough set theory. In: Advances in Machine Learning I, pp. 367–383. Springer (2010)
18.
Caballero, Y., Bello, R., Arco, L., Márquez, Y., León, P., García, M.M., Casas, G.: Rough set theory measures for quality assessment of a training set. In: Granular Computing: At the Junction of Rough Sets and Fuzzy Sets, pp. 199–210. Springer (2008)
19.
Caballero, Y., Joseph, S., Lezcano, Y., Bello, R., Garcia, M.M., Pizano, Y.: Using rough sets to edit training set in k-nn method. In: ISDA, pp. 456–463 (2005)
20.
Chan, C.C.: A rough set approach to attribute generalization in data mining. Inf. Sci. 107(1), 169–176 (1998)MathSciNetCrossRef
21.
Chawla, N.V.: Data mining for imbalanced datasets: an overview. In: Data Mining and Knowledge Discovery Handbook, pp. 853–867. Springer (2005)
22.
Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: Smote: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321–357 (2002)MATH
23.
Chawla, N.V., Cieslak, D.A., Hall, L.O., Joshi, A.: Automatically countering imbalance and its empirical relationship to cost. Data Min. Knowl. Discovery 17(2), 225–252 (2008)MathSciNetCrossRef
24.
Chen, C., Mac Parthaláin, N., Li, Y., Price, C., Quek, C., Shen, Q.: Rough-fuzzy rule interpolation. Inf. Sci. 351, 1–17 (2016)MathSciNetCrossRef
25.
Chen, C.P., Zhang, C.Y.: Data-intensive applications, challenges, techniques and technologies: a survey on big data. Inf. Sci. 275, 314–347 (2014)CrossRef
26.
Chen, C.Y., Li, Z.G., Qiao, S.Y., Wen, S.P.: Study on discretization in rough set based on genetic algorithm. In: 2003 International Conference on Machine Learning and Cybernetics, vol. 3, pp. 1430–1434. IEEE (2003)
27.
Chen, D., Hu, Q., Yang, Y.: Parameterized attribute reduction with gaussian kernel based fuzzy rough sets. Inf. Sci. 181(23), 5169–5179 (2011)MATHCrossRef
28.
Chen, D., Zhang, L., Zhao, S., Hu, Q., Zhu, P.: A novel algorithm for finding reducts with fuzzy rough sets. IEEE Trans. Fuzzy Syst. 20(2), 385–389 (2012)CrossRef
29.
Chen, H., Chiang, R.H., Storey, V.C.: Business intelligence and analytics: from big data to big impact. MIS Q. 36(4), 1165–1188 (2012)
30.
Chen, H., Li, T., Qiao, S., Ruan, D.: A rough set based dynamic maintenance approach for approximations in coarsening and refining attribute values. Int. J. Intell. Syst. 25(10), 1005–1026 (2010)MATHCrossRef
31.
Chen, H., Li, T., Ruan, D.: Dynamic maintenance of approximations under a rough-set based variable precision limited tolerance relation. J. Multiple-Valued Log. Soft Comput. 18 (2012)
32.
Chen, H., Li, T., Ruan, D.: Maintenance of approximations in incomplete ordered decision systems while attribute values coarsening or refining. Knowl. Based Syst. 31, 140–161 (2012)CrossRef
33.
Chen, H., Li, T., Ruan, D., Lin, J., Hu, C.: A rough-set-based incremental approach for updating approximations under dynamic maintenance environments. IEEE Trans. Knowl. Data Eng. 25(2), 274–284 (2013)CrossRef
34.
Chen, Y.S., Cheng, C.H.: A delphi-based rough sets fusion model for extracting payment rules of vehicle license tax in the government sector. Expert Syst. Appl. 37(3), 2161–2174 (2010)CrossRef
35.
Cheng, X., Wu, R.: Clustering path profiles on a website using rough k-means method. J. Comput. Inf. Syst. 8(14), 6009–6016 (2012)
36.
Cheng, Y.: The incremental method for fast computing the rough fuzzy approximations. Data Knowl. Eng. 70(1), 84–100 (2011)CrossRef
37.
Choubey, S.K., Deogun, J.S., Raghavan, V.V., Sever, H.: A comparison of feature selection algorithms in the context of rough classifiers. In: Proceedings of the Fifth IEEE International Conference on Fuzzy Systems, 1996, vol. 2, pp. 1122–1128. IEEE (1996)
38.
Chouchoulas, A., Shen, Q.: A rough set-based approach to text classification. In: International Workshop on Rough Sets, Fuzzy Sets, Data Mining, and Granular-Soft Computing, pp. 118–127. Springer (1999)
39.
Ciucci, D.: Attribute dynamics in rough sets. In: International Symposium on Methodologies for Intelligent Systems, pp. 43–51. Springer (2011)
40.
Ciucci, D.: Temporal dynamics in information tables. Fundamenta Informaticae 115(1), 57–74 (2012)MathSciNetMATH
41.
Coello, L., Fernandez, Y., Filiberto, Y., Bello, R.: Improving the multilayer perceptron learning by using a method to calculate the initial weights with the similarity quality measure based on fuzzy sets and particle swarms. Computación y Sistemas 19(2), 309–320 (2015)CrossRef
42.
Cornelis, C., Jensen, R.: A noise-tolerant approach to fuzzy-rough feature selection. In: IEEE International Conference on Fuzzy Systems, 2008. FUZZ-IEEE 2008. (IEEE World Congress on Computational Intelligence), pp. 1598–1605. IEEE (2008)
43.
Cornelis, C., Jensen, R., Hurtado, G., Śle, D., et al.: Attribute selection with fuzzy decision reducts. Inf. Sci. 180(2), 209–224 (2010)MathSciNetMATHCrossRef
44.
Cornelis, C., Verbiest, N., Jensen, R.: Ordered weighted average based fuzzy rough sets. In: International Conference on Rough Sets and Knowledge Technology, pp. 78–85. Springer (2010)
45.
Crespo, F., Peters, G., Weber, R.: Rough clustering approaches for dynamic environments. In: Rough Sets: Selected Methods and Applications in Management and Engineering, pp. 39–50. Springer (2012)
46.
Dai, J.H., Li, Y.X.: Study on discretization based on rough set theory. In: 2002 International Conference on Machine Learning and Cybernetics, 2002. Proceedings, vol. 3, pp. 1371–1373. IEEE (2002)
47.
De Comité, F., Gilleron, R., Tommasi, M.: Learning multi-label alternating decision trees from texts and data. In: International Workshop on Machine Learning and Data Mining in Pattern Recognition, pp. 35–49. Springer (2003)
48.
Dean, J., Ghemawat, S.: Mapreduce: simplified data processing on large clusters. Commun. ACM 51(1), 107–113 (2008)CrossRef
49.
Delic, D., Lenz, H.J., Neiling, M.: Improving the quality of association rule mining by means of rough sets. In: Soft Methods in Probability, Statistics and Data Analysis, pp. 281–288. Springer (2002)
50.
Deng, D., Huang, H.: Dynamic reduction based on rough sets in incomplete decision systems. In: International Conference on Rough Sets and Knowledge Technology, pp. 76–83. Springer (2007)
51.
Derrac, J., Cornelis, C., García, S., Herrera, F.: Enhancing evolutionary instance selection algorithms by means of fuzzy rough set based feature selection. Inf. Sci. 186(1), 73–92 (2012)CrossRef
52.
Dey, P., Dey, S., Datta, S., Sil, J.: Dynamic discreduction using rough sets. Appl. Soft Comput. 11(5), 3887–3897 (2011)CrossRef
53.
Dougherty, J., Kohavi, R., Sahami, M., et al.: Supervised and unsupervised discretization of continuous features. Machine Learning: Proceedings of the Twelfth International Conference 12, 194–202 (1995)
54.
Dubois, D., Prade, H.: Twofold fuzzy sets and rough sets some issues in knowledge representation. Fuzzy Sets Syst. 23(1), 3–18 (1987)MathSciNetMATHCrossRef
55.
Dubois, D., Prade, H.: Rough fuzzy sets and fuzzy rough sets*. Int. J. Gen. Syst. 17(2–3), 191–209 (1990)MATHCrossRef
56.
Falcon, R., Jeon, G., Bello, R., Jeong, J.: Rough clustering with partial supervision. In: Rough Set Theory: A True Landmark in Data Analysis, pp. 137–161. Springer (2009)
57.
Falcon, R., Nayak, A., Abielmona, R.: An Online shadowed clustering algorithm applied to risk visualization in territorial security. In: IEEE Symposium on Computational Intelligence for Security and Defense Applications (CISDA), pp. 1–8. Ottawa, Canada (2012)
58.
Fan, Y.N., Chern, C.C.: An agent model for incremental rough set-based rule induction: a Big Data analysis in sales promotion. In: 2013 46th Hawaii International Conference on System Sciences (HICSS), pp. 985–994. IEEE (2013)
59.
Fan, Y.N., Tseng, T.L.B., Chern, C.C., Huang, C.C.: Rule induction based on an incremental rough set. Expert Syst. Appl. 36(9), 11439–11450 (2009)CrossRef
60.
Fernández, A., del Río, S., López, V., Bawakid, A., del Jesus, M.J., Benítez, J.M., Herrera, F.: Big data with cloud computing: an insight on the computing environment, mapreduce, and programming frameworks. Wiley Interdisc. Rev. Data Min. Knowl. Discovery 4(5), 380–409 (2014)
61.
Filiberto, Y., Caballero, Y., Larrua, R., Bello, R.: A method to build similarity relations into extended rough set theory. In: 2010 10th International Conference on Intelligent Systems Design and Applications, pp. 1314–1319. IEEE (2010)
62.
Filiberto Cabrera, Y., Caballero Mota, Y., Bello Pérez, R., Frías, M.: Algoritmo para el aprendizaje de reglas de clasificación basado en la teoría de los conjuntos aproximados extendida. Dyna; vol. 78, núm. 169 (2011); 62-70 DYNA; vol. 78, núm. 169 (2011); 62-70 2346-2183 0012-7353 (2011)
63.
Gogoi, P., Bhattacharyya, D.K., Kalita, J.K.: A rough set-based effective rule generation method for classification with an application in intrusion detection. Int. J. Secur. Netw. 8(2), 61–71 (2013)CrossRef
64.
Gómez, Y., Bello, R., Puris, A., Garcia, M.M., Nowe, A.: Two step swarm intelligence to solve the feature selection problem. J. UCS 14(15), 2582–2596 (2008)
65.
Greco, S., Matarazzo, B., Słowiński, R.: Parameterized rough set model using rough membership and bayesian confirmation measures. Int. J. Approximate Reasoning 49(2), 285–300 (2008)MathSciNetMATHCrossRef
66.
Greco, S., Słowiński, R., Stefanowski, J., Żurawski, M.: Incremental versus non-incremental rule induction for multicriteria classification. In: Transactions on Rough Sets II, pp. 33–53. Springer (2004)
67.
Grzymala-Busse, J.W.: LERS—a system for learning from examples based on rough sets. In: Intelligent decision support, pp. 3–18. Springer (1992)
68.
Grzymała-Busse, J.W.: Characteristic relations for incomplete data: A generalization of the indiscernibility relation. In: International Conference on Rough Sets and Current Trends in Computing, pp. 244–253. Springer (2004)
69.
Grzymala-Busse, J.W., Grzymala-Busse, W.J.: Inducing better rule sets by adding missing attribute values. In: International Conference on Rough Sets and Current Trends in Computing, pp. 160–169. Springer (2008)
70.
Guan, J., Bell, D.A., Liu, D.: The rough set approach to association rule mining. In: Third IEEE International Conference on Data Mining, 2003. ICDM 2003, pp. 529–532. IEEE (2003)
71.
Hartigan, J.A., Wong, M.A.: Algorithm as 136: a k-means clustering algorithm. J. Roy. Stat. Soc. Ser. C (Appl. Stat.) 28(1), 100–108 (1979)
72.
Hassanein, W., Elmelegy, A.A.: An algorithm for selecting clustering attribute using significance of attributes. Int. J. Database Theory Appl. 6(5), 53–66 (2013)CrossRef
73.
He, H., Chen, S., Li, K., Xu, X.: Incremental learning from stream data. IEEE Trans. Neural Netw. 22(12), 1901–1914 (2011)CrossRef
74.
He, H., Min, F., Zhu, W.: Attribute reduction in test-cost-sensitive decision systems with common-test-costs. In: Proceedings of the 3rd International Conference on Machine Learning and Computing, vol. 1, pp. 432–436 (2011)
75.
He, Q., Wu, C., Chen, D., Zhao, S.: Fuzzy rough set based attribute reduction for information systems with fuzzy decisions. Knowl. Based Syst. 24(5), 689–696 (2011)CrossRef
76.
Herawan, T.: Rough set approach for categorical data clustering. Ph.D. thesis, Universiti Tun Hussein Onn Malaysia (2010)
77.
Herawan, T., Deris, M.M., Abawajy, J.H.: A rough set approach for selecting clustering attribute. Knowl. Based Syst. 23(3), 220–231 (2010)CrossRef
78.
Hirano, S., Tsumoto, S.: Rough clustering and its application to medicine. J. Inf. Sci. 124, 125–137 (2000)CrossRef
79.
Ho, T.K., Basu, M.: Complexity measures of supervised classification problems. IEEE Trans. Pattern Anal. Mach. Intell. 24(3), 289–300 (2002)CrossRef
80.
Hong, T.P., Tseng, L.H., Wang, S.L.: Learning rules from incomplete training examples by rough sets. Expert Syst. Appl. 22(4), 285–293 (2002)CrossRef
81.
82.
Hu, F., Wang, G., Huang, H., Wu, Y.: Incremental attribute reduction based on elementary sets. In: International Workshop on Rough Sets, Fuzzy Sets, Data Mining, and Granular-Soft Computing, pp. 185–193. Springer (2005)
83.
Hu, H., Shi, Z.: Machine learning as granular computing. In: IEEE International Conference on Granular Computing, 2009, GRC’09, pp. 229–234. IEEE (2009)
84.
Hu, Q., Che, X., Zhang, L., Zhang, D., Guo, M., Yu, D.: Rank entropy-based decision trees for monotonic classification. IEEE Trans. Knowl. Data Eng. 24(11), 2052–2064 (2012)CrossRef
85.
Hu, Q., Liu, J., Yu, D.: Mixed feature selection based on granulation and approximation. Knowl. Based Syst. 21(4), 294–304 (2008)CrossRef
86.
Hu, Q., Pan, W., Zhang, L., Zhang, D., Song, Y., Guo, M., Yu, D.: Feature selection for monotonic classification. IEEE Trans. Fuzzy Syst. 20(1), 69–81 (2012)CrossRef
87.
Hu, Q., Xie, Z., Yu, D.: Hybrid attribute reduction based on a novel fuzzy-rough model and information granulation. Pattern Recogn. 40(12), 3509–3521 (2007)MATHCrossRef
88.
Hu, Q., Yu, D.: An improved clustering algorithm for information granulation. In: International Conference on Fuzzy Systems and Knowledge Discovery, pp. 494–504. Springer (2005)
89.
Hu, Q., Yu, D., Liu, J., Wu, C.: Neighborhood rough set based heterogeneous feature subset selection. Inf. Sci. 178(18), 3577–3594 (2008)MathSciNetMATHCrossRef
90.
Hu, Q., Yu, D., Xie, Z.: Information-preserving hybrid data reduction based on fuzzy-rough techniques. Pattern Recogn. Lett. 27(5), 414–423 (2006)CrossRef
91.
Hu, Q., Yu, D., Xie, Z., Liu, J.: Fuzzy probabilistic approximation spaces and their information measures. IEEE Trans. Fuzzy Syst. 14(2), 191–201 (2006)CrossRef
92.
Hu, Q., Zhang, L., An, S., Zhang, D., Yu, D.: On robust fuzzy rough set models. IEEE Trans. Fuzzy Syst. 20(4), 636–651 (2012)CrossRef
93.
Huang, C.C., Tseng, T.L.B., Fan, Y.N., Hsu, C.H.: Alternative rule induction methods based on incremental object using rough set theory. Appl. Soft Comput. 13(1), 372–389 (2013)CrossRef
94.
Huang, Z., Hu, Y.Q.: Applying AI technology and rough set theory to mine association rules for supporting knowledge management. In: 2003 International Conference on Machine Learning and Cybernetics, vol. 3, pp. 1820–1825. IEEE (2003)
95.
Hüllermeier, E.: Granular computing in machine learning and data mining. In: Handbook of Granular Computing, pp. 889–906 (2008)
96.
Jain, A.K., Murty, M.N., Flynn, P.J.: Data clustering: a review. ACM Comput. Surv. (CSUR) 31(3), 264–323 (1999)CrossRef
97.
Janusz, A., Slezak, D.: Rough set methods for attribute clustering and selection. Appl. Artif. Intell. 28(3), 220–242 (2014)CrossRef
98.
Janusz, A., Stawicki, S.: Applications of approximate reducts to the feature selection problem. In: International Conference on Rough Sets and Knowledge Technology, pp. 45–50. Springer (2011)
99.
Jensen, R., Cornelis, C.: Fuzzy-rough instance selection. In: 2010 IEEE International Conference on Fuzzy Systems (FUZZ), pp. 1–7. IEEE (2010)
100.
Jensen, R., Cornelis, C., Shen, Q.: Hybrid fuzzy-rough rule induction and feature selection. In: IEEE International Conference on Fuzzy Systems, 2009. FUZZ-IEEE 2009, pp. 1151–1156. IEEE (2009)
101.
Jensen, R., Shen, Q.: Fuzzy-rough sets for descriptive dimensionality reduction. In: Proceedings of the 2002 IEEE International Conference on Fuzzy Systems, 2002. FUZZ-IEEE’02, vol. 1, pp. 29–34. IEEE (2002)
102.
Jensen, R., Shen, Q.: Finding rough set reducts with ant colony optimization. In: Proceedings of the 2003 UK Workshop on Computational Intelligence, vol. 1, pp. 15–22 (2003)
103.
Jensen, R., Shen, Q.: Fuzzy-rough attribute reduction with application to web categorization. Fuzzy Sets Syst. 141(3), 469–485 (2004)MathSciNetMATHCrossRef
104.
Jensen, R., Shen, Q.: Semantics-preserving dimensionality reduction: rough and fuzzy-rough-based approaches. IEEE Trans. Knowl. Data Eng. 16(12), 1457–1471 (2004)CrossRef
105.
Jensen, R., Shen, Q.: New approaches to fuzzy-rough feature selection. IEEE Trans. Fuzzy Syst. 17(4), 824–838 (2009)CrossRef
106.
Jia, X., Liao, W., Tang, Z., Shang, L.: Minimum cost attribute reduction in decision-theoretic rough set models. Inf. Sci. 219, 151–167 (2013)MathSciNetMATHCrossRef
107.
Jia, X., Shang, L., Zhou, B., Yao, Y.: Generalized attribute reduct in rough set theory. Knowl. Based Syst. 91, 204–218 (2016)CrossRef
108.
Jiang, F., Sui, Y., Cao, C.: Outlier detection based on rough membership function. In: International Conference on Rough Sets and Current Trends in Computing, pp. 388–397. Springer (2006)
109.
Jiang, F., Sui, Y., Cao, C.: Some issues about outlier detection in rough set theory. Expert Syst. Appl. 36(3), 4680–4687 (2009)CrossRef
110.
Jiang, Y.C., Liu, Y.Z., Liu, X., Zhang, J.K.: Constructing associative classifier using rough sets and evidence theory. In: International Workshop on Rough Sets, Fuzzy Sets, Data Mining, and Granular-Soft Computing, pp. 263–271. Springer (2007)
111.
Jiao, X., Lian-cheng, X., Lin, Q.: Association rules mining algorithm based on rough set. In: International Symposium on Information Technology in Medicine and Education, Print ISBN, pp. 978–1 (2012)
112.
Joshi, P., Kulkarni, P.: Incremental learning: areas and methods—a survey. Int. J. Data Min. Knowl. Manage. Process 2(5), 43 (2012)
113.
Ju, H., Yang, X., Song, X., Qi, Y.: Dynamic updating multigranulation fuzzy rough set: approximations and reducts. Int. J. Mach. Learn. Cybern. 5(6), 981–990 (2014)CrossRef
114.
Ju, H., Yang, X., Yang, P., Li, H., Zhou, X.: A moderate attribute reduction approach in decision-theoretic rough set. In: Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing, pp. 376–388. Springer (2015)
115.
Ju, H., Yang, X., Yu, H., Li, T., Yu, D.J., Yang, J.: Cost-sensitive rough set approach. Inf. Sci. 355, 282–298 (2016)CrossRef
116.
Jun, Z., Zhou, Y.H.: New heuristic method for data discretization based on rough set theory. J. China Univ. Posts Telecommun. 16(6), 113–120 (2009)CrossRef
117.
Kambatla, K., Kollias, G., Kumar, V., Grama, A.: Trends in big data analytics. J. Parallel Distrib. Comput. 74(7), 2561–2573 (2014)CrossRef
118.
Kaneiwa, K.: A rough set approach to mining connections from information systems. In: Proceedings of the 2010 ACM Symposium on Applied Computing, pp. 990–996. ACM (2010)
119.
Ke, L., Feng, Z., Ren, Z.: An efficient ant colony optimization approach to attribute reduction in rough set theory. Pattern Recogn. Lett. 29(9), 1351–1357 (2008)CrossRef
120.
Komorowski, J., Pawlal, Z., Polkowski, L., Skowron, A.: A rough set perspective on data and knowledge. In: The Handbook of Data Mining and Knowledge Discovery. Oxford University Press, Oxford (1999)
121.
122.
Kumar, P., Krishna, P.R., Bapi, R.S., De, S.K.: Rough clustering of sequential data. Data Knowl. Eng. 63(2), 183–199 (2007)CrossRef
123.
Kumar, P., Vadakkepat, P., Poh, L.A.: Fuzzy-rough discriminative feature selection and classification algorithm, with application to microarray and image datasets. Appl. Soft Comput. 11(4), 3429–3440 (2011)CrossRef
124.
Kumar, P., Wasan, S.K.: Comparative study of k-means, pam and rough k-means algorithms using cancer datasets. In: Proceedings of CSIT: 2009 International Symposium on Computing, Communication, and Control (ISCCC 2009), vol. 1, pp. 136–140 (2011)
125.
126.
Lai, J.Z., Juan, E.Y., Lai, F.J.: Rough clustering using generalized fuzzy clustering algorithm. Pattern Recogn. 46(9), 2538–2547 (2013)CrossRef
127.
Lee, S.C., Huang, M.J.: Applying ai technology and rough set theory for mining association rules to support crime management and fire-fighting resources allocation. J. Inf. Technol. Soc. 2(65), 65–78 (2002)
128.
Lenarcik, A., Piasta, Z.: Discretization of condition attributes space. In: Intelligent Decision Support, pp. 373–389. Springer (1992)
129.
Leung, Y., Fischer, M.M., Wu, W.Z., Mi, J.S.: A rough set approach for the discovery of classification rules in interval-valued information systems. Int. J. Approximate Reasoning 47(2), 233–246 (2008)MathSciNetMATHCrossRef
130.
Li, F., Ye, M., Chen, X.: An extension to rough c-means clustering based on decision-theoretic rough sets model. Int. J. Approximate Reasoning 55(1), 116–129 (2014)MathSciNetMATHCrossRef
131.
Li, H., Li, D., Zhai, Y., Wang, S., Zhang, J.: A variable precision attribute reduction approach in multilabel decision tables. Sci. World J. 2014 (2014)
132.
Li, H., Zhang, L., Huang, B., Zhou, X.: Sequential three-way decision and granulation for cost-sensitive face recognition. Knowl. Based Syst. 91, 241–251 (2016)CrossRef
133.
Li, H., Zhou, X., Zhao, J., Liu, D.: Non-monotonic attribute reduction in decision-theoretic rough sets. Fundamenta Informaticae 126(4), 415–432 (2013)MathSciNetMATH
134.
Li, J., Cercone, N.: A rough set based model to rank the importance of association rules. In: International Workshop on Rough Sets, Fuzzy Sets, Data Mining, and Granular-Soft Computing, pp. 109–118. Springer (2005)
135.
Li, M., Deng, S., Wang, L., Feng, S., Fan, J.: Hierarchical clustering algorithm for categorical data using a probabilistic rough set model. Knowl. Based Syst. 65, 60–71 (2014)CrossRef
136.
Li, M., Shang, C., Feng, S., Fan, J.: Quick attribute reduction in inconsistent decision tables. Inf. Sci. 254, 155–180 (2014)MathSciNetMATHCrossRef
137.
Li, S., Li, T., Liu, D.: Dynamic maintenance of approximations in dominance-based rough set approach under the variation of the object set. Int. J. Intell. Syst. 28(8), 729–751 (2013)MathSciNetCrossRef
138.
Li, S., Li, T., Liu, D.: Incremental updating approximations in dominance-based rough sets approach under the variation of the attribute set. Knowl. Based Syst. 40, 17–26 (2013)CrossRef
139.
Li, T., Ruan, D., Geert, W., Song, J., Xu, Y.: A rough sets based characteristic relation approach for dynamic attribute generalization in data mining. Knowl. Based Syst. 20(5), 485–494 (2007)CrossRef
140.
Liang, J., Wang, F., Dang, C., Qian, Y.: A group incremental approach to feature selection applying rough set technique. IEEE Trans. Knowl. Data Eng. 26(2), 294–308 (2014)CrossRef
141.
Lin, T.Y., Yao, Y.Y., Zadeh, L.A.: Data mining, rough sets and granular computing. Physica 95 (2013)
142.
Lingras, P.: Unsupervised rough set classification using gas. J. Intell. Inf. Syst. 16(3), 215–228 (2001)MATHCrossRef
143.
Lingras, P., Chen, M., Miao, D.: Rough cluster quality index based on decision theory. IEEE Trans. Knowl. Data Eng. 21(7), 1014–1026 (2009)CrossRef
144.
Lingras, P., Chen, M., Miao, D.: Qualitative and quantitative combinations of crisp and rough clustering schemes using dominance relations. Int. J. Approximate Reasoning 55(1), 238–258 (2014)MathSciNetMATHCrossRef
145.
Lingras, P., West, C.: Interval set clustering of web users with rough k-means. J. Intell. Inf. Syst. 23(1), 5–16 (2004)MATHCrossRef
146.
Liu, D., Li, T., Liu, G., Hu, P.: An approach for inducing interesting incremental knowledge based on the change of attribute values. In: IEEE International Conference on Granular Computing, 2009, GRC’09, pp. 415–418. IEEE (2009)
147.
Liu, D., Li, T., Ruan, D., Zhang, J.: Incremental learning optimization on knowledge discovery in dynamic business intelligent systems. J. Glob. Optim. 51(2), 325–344 (2011)MathSciNetMATHCrossRef
148.
Liu, D., Li, T., Ruan, D., Zou, W.: An incremental approach for inducing knowledge from dynamic information systems. Fundamenta Informaticae 94(2), 245–260 (2009)MathSciNetMATH
149.
Liu, D., Li, T., Zhang, J.: A rough set-based incremental approach for learning knowledge in dynamic incomplete information systems. Int. J. Approximate Reasoning 55(8), 1764–1786 (2014)MathSciNetMATHCrossRef
150.
Liu, D., Li, T., Zhang, J.: Incremental updating approximations in probabilistic rough sets under the variation of attributes. Knowl. Based Syst. 73, 81–96 (2015)CrossRef
151.
Liu, D., Liang, D.: Incremental learning researches on rough set theory: status and future. Int. J. Rough Sets Data Anal. (IJRSDA) 1(1), 99–112 (2014)MathSciNetCrossRef
152.
Liu, J., Hu, Q., Yu, D.: A comparative study on rough set based class imbalance learning. Knowl. Based Syst. 21(8), 753–763 (2008)CrossRef
153.
Liu, J., Hu, Q., Yu, D.: A weighted rough set based method developed for class imbalance learning. Inf. Sci. 178(4), 1235–1256 (2008)MathSciNetMATHCrossRef
154.
Liu, Y., Xu, C., Zhang, Q., Pan, Y.: Rough rule extracting from various conditions: Incremental and approximate approaches for inconsistent data. Fundamenta Informaticae 84(3, 4), 403–427 (2008)
155.
Lu, J., Tan, Y.P.: Cost-sensitive subspace analysis and extensions for face recognition. IEEE Trans. Inf. Forensics Secur. 8(3), 510–519 (2013)CrossRef
156.
Luo, C., Li, T., Chen, H., Liu, D.: Incremental approaches for updating approximations in set-valued ordered information systems. Knowl. Based Syst. 50, 218–233 (2013)CrossRef
157.
Luo, C., Li, T., Yi, Z., Fujita, H.: Matrix approach to decision-theoretic rough sets for evolving data. Knowl. Based Syst. 99, 123–134 (2016)CrossRef
158.
Ma, T., Tang, M.: Weighted rough set model. In: Sixth International Conference on Intelligent Systems Design and Applications, vol. 1, pp. 481–485. IEEE (2006)
159.
Maji, P., Garai, P.: Fuzzy-rough simultaneous attribute selection and feature extraction algorithm. IEEE Trans. Cybern. 43(4), 1166–1177 (2013)CrossRef
160.
Maji, P., Pal, S.K.: RFCM: a hybrid clustering algorithm using rough and fuzzy sets. Fundamenta Informaticae 80(4), 475–496 (2007)MathSciNetMATH
161.
Mak, B., Munakata, T.: Rule extraction from expert heuristics: a comparative study of rough sets with neural networks and ID3. Eur. J. Oper. Res. 136(1), 212–229 (2002)MATHCrossRef
162.
Miao, D., Chen, M., Wei, Z., Duan, Q.: A reasonable rough approximation for clustering web users. In: International Workshop on Web Intelligence Meets Brain Informatics, pp. 428–442. Springer (2006)
163.
Min, F., He, H., Qian, Y., Zhu, W.: Test-cost-sensitive attribute reduction. Inf. Sci. 181(22), 4928–4942 (2011)CrossRef
164.
165.
166.
167.
Mirkin, B.: Mathematical classification and clustering: from how to what and why. In: Classification, Data Analysis, and Data Highways, pp. 172–181. Springer (1998)
168.
Mitra, S.: An evolutionary rough partitive clustering. Pattern Recogn. Lett. 25(12), 1439–1449 (2004)CrossRef
169.
Mitra, S., Banka, H.: Application of rough sets in pattern recognition. In: Transactions on Rough Sets VII, pp. 151–169. Springer (2007)
170.
Mitra, S., Banka, H., Pedrycz, W.: Rough-fuzzy collaborative clustering. IEEE Trans. Syst. Man, Cybern. Part B (Cybern.) 36(4), 795–805 (2006)
171.
Mitra, S., Barman, B.: Rough-fuzzy clustering: an application to medical imagery. In: International Conference on Rough Sets and Knowledge Technology, pp. 300–307. Springer (2008)
172.
173.
Nguyen, H.S.: Discretization problem for rough sets methods. In: International Conference on Rough Sets and Current Trends in Computing, pp. 545–552. Springer (1998)
174.
Nguyen, H.S.: On efficient handling of continuous attributes in large data bases. Fundamenta Informaticae 48(1), 61–81 (2001)MathSciNetMATH
175.
Orlowska, E.: Dynamic information systems. Institute of Computer Science, Polish Academy of Sciences (1981)MATH
176.
Ozawa, S., Pang, S., Kasabov, N.: Incremental learning of chunk data for online pattern classification systems. IEEE Trans. Neural Netw. 19(6), 1061–1074 (2008)CrossRef
177.
Park, I.K., Choi, G.S.: Rough set approach for clustering categorical data using information-theoretic dependency measure. Inf. Syst. 48, 289–295 (2015)CrossRef
178.
Parmar, D., Wu, T., Blackhurst, J.: MMR: an algorithm for clustering categorical data using rough set theory. Data Knowl Eng. 63(3), 879–893 (2007)CrossRef
179.
180.
181.
182.
Pawlak, Z., Wong, S.K.M., Ziarko, W.: Rough sets: probabilistic versus deterministic approach. Int. J. Man-Mach. Stud. 29(1), 81–95 (1988)MATHCrossRef
183.
Pedrycz, W.: Granular Computing: An Emerging Paradigm, vol. 70. Springer Science & Business Media (2001)
184.
Peters, G.: Outliers in rough k-means clustering. In: International Conference on Pattern Recognition and Machine Intelligence, pp. 702–707. Springer (2005)
185.
Peters, G.: Some refinements of rough k-means clustering. Pattern Recogn. 39(8), 1481–1491 (2006)MATHCrossRef
186.
187.
Peters, G.: Is there any need for rough clustering? Pattern Recogn. Lett. 53, 31–37 (2015)CrossRef
188.
Peters, G., Crespo, F., Lingras, P., Weber, R.: Soft clustering-fuzzy and rough approaches and their extensions and derivatives. Int. J. Approximate Reasoning 54(2), 307–322 (2013)MathSciNetCrossRef
189.
Peters, G., Lampart, M., Weber, R.: Evolutionary rough k-medoid clustering. In: Transactions on Rough Sets VIII, pp. 289–306. Springer (2008)
190.
Peters, G., Weber, R., Nowatzke, R.: Dynamic rough clustering and its applications. Appl. Soft Comput. 12(10), 3193–3207 (2012)CrossRef
191.
Pradeepa, A., Selvadoss ThanamaniLee, A.: Hadoop file system and fundamental concept of mapreduce interior and closure rough set approximations. Int. J. Adv. Res. Comput. Commun. Eng. 2 (2013)
192.
do Prado, H.A., Engel, P.M., Chaib Filho, H.: Rough clustering: an alternative to find meaningful clusters by using the reducts from a dataset. In: International Conference on Rough Sets and Current Trends in Computing, pp. 234–238. Springer (2002)
193.
Qian, Y., Wang, Q., Cheng, H., Liang, J., Dang, C.: Fuzzy-rough feature selection accelerator. Fuzzy Sets Syst. 258, 61–78 (2015)MathSciNetMATHCrossRef
194.
Ramentol, E., Caballero, Y., Bello, R., Herrera, F.: Smote-rsb*: a hybrid preprocessing approach based on oversampling and undersampling for high imbalanced data-sets using smote and rough sets theory. Knowl. Inf. Syst. 33(2), 245–265 (2012)CrossRef
195.
Riza, L.S., Janusz, A., Bergmeir, C., Cornelis, C., Herrera, F., Śle, D., Benítez, J.M., 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
196.
Salamó, M., López-Sánchez, M.: Rough set based approaches to feature selection for case-based reasoning classifiers. Pattern Recogn. Lett. 32(2), 280–292 (2011)CrossRef
197.
Salido, J.F., Murakami, S.: Rough set analysis of a general type of fuzzy data using transitive aggregations of fuzzy similarity relations. Fuzzy Sets Syst. 139(3), 635–660 (2003)MathSciNetMATHCrossRef
198.
Schapire, R.E., Singer, Y.: Boostexter: a boosting-based system for text categorization. Mach. Learn. 39(2–3), 135–168 (2000)MATHCrossRef
199.
Shan, N., Ziarko, W.: Data-based acquisition and incremental modification of classification rules. Comput. Intell. 11(2), 357–370 (1995)CrossRef
200.
Shen, F., Yu, H., Kamiya, Y., Hasegawa, O.: An online incremental semi-supervised learning method. JACIII 14(6), 593–605 (2010)CrossRef
201.
Shen, Q., Chouchoulas, A.: Combining rough sets and data-driven fuzzy learning for generation of classification rules. Pattern Recogn. 32(12), 2073–2076 (1999)CrossRef
202.
Shen, Q., Chouchoulas, A.: A modular approach to generating fuzzy rules with reduced attributes for the monitoring of complex systems. Eng. Appl. Artif. Intell. 13(3), 263–278 (2000)CrossRef
203.
Shen, Q., Jensen, R.: Selecting informative features with fuzzy-rough sets and its application for complex systems monitoring. Pattern Recogn. 37(7), 1351–1363 (2004)MATHCrossRef
204.
Shu, W., Shen, H.: Incremental feature selection based on rough set in dynamic incomplete data. Pattern Recogn. 47(12), 3890–3906 (2014)CrossRef
205.
Singh, G.K., Minz, S.: Discretization using clustering and rough set theory. In: International Conference on Computing: Theory and Applications, 2007. ICCTA’07, pp. 330–336. IEEE (2007)
206.
Skowron, A., Rauszer, C.: The discernibility matrices and functions in information systems. In: Intelligent Decision Support, pp. 331–362. Springer (1992)
207.
Skowron, A., Stepaniuk, J.: Tolerance approximation spaces. Fundamenta Informaticae 27(2, 3), 245–253 (1996)
208.
Slezak, D.: Approximate bayesian networks. In: Technologies for Constructing Intelligent Systems 2, pp. 313–325. Springer (2002)
209.
Ślezak, D.: Approximate entropy reducts. Fundamenta Informaticae 53(3–4), 365–390 (2002)MathSciNetMATH
210.
Slezak, D., Ziarko, W., et al.: The investigation of the bayesian rough set model. Int. J. Approximate Reasoning 40(1), 81–91 (2005)MathSciNetMATHCrossRef
211.
212.
Slowinski, R., Vanderpooten, D., et al.: A generalized definition of rough approximations based on similarity. IEEE Trans. Knowl. Data Eng. 12(2), 331–336 (2000)CrossRef
213.
Soni, R., Nanda, R.: Neighborhood clustering of web users with rough k-means. In: Proceedings of 6th WSEAS International Conference on Circuits, Systems, Electronics, Control & Signal Processing, pp. 570–574 (2007)
214.
Stefanowski, J.: The rough set based rule induction technique for classification problems. In: In Proceedings of 6th European Conference on Intelligent Techniques and Soft Computing EUFIT, vol. 98 (1998)
215.
Stefanowski, J.: On combined classifiers, rule induction and rough sets. In: Transactions on Rough Sets VI, pp. 329–350. Springer (2007)
216.
Stefanowski, J., Vanderpooten, D.: Induction of decision rules in classification and discovery-oriented perspectives. Int. J. Intell. Syst. 16(1), 13–27 (2001)MATHCrossRef
217.
Stefanowski, J., Wilk, S.: Rough sets for handling imbalanced data: combining filtering and rule-based classifiers. Fundamenta Informaticae 72(1–3), 379–391 (2006)MATH
218.
Stefanowski, J., Wilk, S.: Extending rule-based classifiers to improve recognition of imbalanced classes. In: Advances in Data Management, pp. 131–154. Springer (2009)
219.
Su, C.T., Hsu, J.H.: An extended Chi2 algorithm for discretization of real value attributes. IEEE Trans. Knowl. Data Eng. 17(3), 437–441 (2005)CrossRef
220.
Su, C.T., Hsu, J.H.: Precision parameter in the variable precision rough sets model: an application. Omega 34(2), 149–157 (2006)MathSciNetCrossRef
221.
222.
Świniarski, R.W.: Rough sets methods in feature reduction and classification. Int. J. Appl. Math. Comput. Sci. 11(3), 565–582 (2001)MathSciNetMATH
223.
Swiniarski, R.W., Skowron, A.: Rough set methods in feature selection and recognition. Pattern Recogn. Lett. 24(6), 833–849 (2003)MATHCrossRef
224.
Tay, F.E., Shen, L.: Economic and financial prediction using rough sets model. Eur. J. Oper. Res. 141(3), 641–659 (2002)MATHCrossRef
225.
Tsang, E.C., Chen, D., Yeung, D.S., Wang, X.Z., Lee, J.W.: Attributes reduction using fuzzy rough sets. IEEE Trans. Fuzzy Syst. 16(5), 1130–1141 (2008)CrossRef
226.
Tsoumakas, G., Katakis, I.: Multi-label classification: an overview. Aristotle University of Thessaloniki, Greece, Deparment of Informatics (2006)
227.
Tsoumakas, G., Vlahavas, I.: Random k-labelsets: an ensemble method for multilabel classification. In: European Conference on Machine Learning, pp. 406–417. Springer (2007)
228.
Tsumoto, S.: Automated extraction of medical expert system rules from clinical databases based on rough set theory. Inf. Sci. 112(1), 67–84 (1998)CrossRef
229.
Tsumoto, S.: Automated extraction of hierarchical decision rules from clinical databases using rough set model. Expert Syst. Appl. 24(2), 189–197 (2003)CrossRef
230.
Tsumoto, S.: Incremental rule induction based on rough set theory. In: International Symposium on Methodologies for Intelligent Systems, pp. 70–79. Springer (2011)
231.
Vanderpooten, D.: Similarity relation as a basis for rough approximations. Adv. Mach. Intell. Soft Comput. 4, 17–33 (1997)
232.
Verbiest, N.: Fuzzy rough and evolutionary approaches to instance selection. Ph.D. thesis, Ghent University (2014)
233.
Verbiest, N., Cornelis, C., Herrera, F.: FRPS: a fuzzy rough prototype selection method. Pattern Recogn. 46(10), 2770–2782 (2013)MATHCrossRef
234.
Vilalta, R., Drissi, Y.: A perspective view and survey of meta-learning. Artif. Intell. Rev. 18(2), 77–95 (2002)CrossRef
235.
Voges, K., Pope, N., Brown, M.: A rough cluster analysis of shopping orientation data. In: Proceedings Australian and New Zealand Marketing Academy Conference, Adelaide, pp. 1625–1631 (2003)
236.
Voges, K.E., Pope, N., Brown, M.R.: Cluster analysis of marketing data examining on-line shopping orientation: a comparison of k-means and rough clustering approaches. In: Heuristics and Optimization for Knowledge Discovery, pp. 207–224 (2002)
237.
Wang, F., Liang, J., Dang, C.: Attribute reduction for dynamic data sets. Applied Soft Computing 13(1), 676–689 (2013)CrossRef
238.
Wang, F., Liang, J., Qian, Y.: Attribute reduction: a dimension incremental strategy. Knowl. Based Syst. 39, 95–108 (2013)CrossRef
239.
Wang, G., Yu, H., Li, T., et al.: Decision region distribution preservation reduction in decision-theoretic rough set model. Inf. Sci. 278, 614–640 (2014)MathSciNetMATHCrossRef
240.
Wang, X., An, S., Shi, H., Hu, Q.: Fuzzy rough decision trees for multi-label classification. In: Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing, pp. 207–217. Springer (2015)
241.
Wang, X., Yang, J., Peng, N., Teng, X.: Finding minimal rough set reducts with particle swarm optimization. In: International Workshop on Rough Sets, Fuzzy Sets, Data Mining, and Granular-Soft Computing, pp. 451–460. Springer (2005)
242.
Wang, X., Yang, J., Teng, X., Xia, W., Jensen, R.: Feature selection based on rough sets and particle swarm optimization. Pattern Recogn. Lett. 28(4), 459–471 (2007)CrossRef
243.
Wei, M.H., Cheng, C.H., Huang, C.S., Chiang, P.C.: Discovering medical quality of total hip arthroplasty by rough set classifier with imbalanced class. Qual. Quant. 47(3), 1761–1779 (2013)CrossRef
244.
Wojna, A.: Constraint based incremental learning of classification rules. In: International Conference on Rough Sets and Current Trends in Computing, pp. 428–435. Springer (2000)
245.
Wróblewski, J.: Finding minimal reducts using genetic algorithms. In: Proceedings of the Second Annual Join Conference on Information Science, pp. 186–189 (1995)
246.
Wróblewski, J.: Theoretical foundations of order-based genetic algorithms. Fundamenta Informaticae 28(3, 4), 423–430 (1996)
247.
Wróblewski, J.: Ensembles of classifiers based on approximate reducts. Fundamenta Informaticae 47(3–4), 351–360 (2001)MathSciNetMATH
248.
Wu, Q., Bell, D.: Multi-knowledge extraction and application. In: International Workshop on Rough Sets, Fuzzy Sets, Data Mining, and Granular-Soft Computing, pp. 274–278. Springer (2003)
249.
Xie, H., Cheng, H.Z., Niu, D.X.: Discretization of continuous attributes in rough set theory based on information entropy. Chin. J. Comput. Chin. Ed. 28(9), 1570 (2005)
250.
Xu, Y., Wang, L., Zhang, R.: A dynamic attribute reduction algorithm based on 0–1 integer programming. Knowl. Based Syst. 24(8), 1341–1347 (2011)CrossRef
251.
Xu, Z., Liang, J., Dang, C., Chin, K.: Inclusion degree: a perspective on measures for rough set data analysis. Inf. Sci. 141(3), 227–236 (2002)MathSciNetMATHCrossRef
252.
Yang, Q., Ling, C., Chai, X., Pan, R.: Test-cost sensitive classification on data with missing values. IEEE Trans. Knowl. Data Eng. 18(5), 626–638 (2006)CrossRef
253.
Yang, X., Qi, Y., Song, X., Yang, J.: Test cost sensitive multigranulation rough set: model and minimal cost selection. Inf. Sci. 250, 184–199 (2013)MathSciNetMATHCrossRef
254.
Yang, X., Qi, Y., Yu, H., Song, X., Yang, J.: Updating multigranulation rough approximations with increasing of granular structures. Knowl. Based Syst. 64, 59–69 (2014)CrossRef
255.
Yang, Y., Chen, D., Dong, Z.: Novel algorithms of attribute reduction with variable precision rough set model. Neurocomputing 139, 336–344 (2014)CrossRef
256.
Yang, Y., Chen, Z., Liang, Z., Wang, G.: Attribute reduction for massive data based on rough set theory and mapreduce. In: International Conference on Rough Sets and Knowledge Technology, pp. 672–678. Springer (2010)
257.
Yao, J., Yao, Y.: A granular computing approach to machine learning. FSKD 2, 732–736 (2002)
258.
Yao, Y.: Combination of rough and fuzzy sets based on \(\alpha \)-level sets. In: Rough sets and Data Mining, pp. 301–321. Springer (1997)
259.
Yao, Y.: Decision-theoretic rough set models. In: International Conference on Rough Sets and Knowledge Technology, pp. 1–12. Springer (2007)
260.
Yao, Y.: Three-way decision: an interpretation of rules in rough set theory. In: International Conference on Rough Sets and Knowledge Technology, pp. 642–649. Springer (2009)
261.
262.
263.
Yao, Y.: An outline of a theory of three-way decisions. In: International Conference on Rough Sets and Current Trends in Computing, pp. 1–17. Springer (2012)
264.
Yao, Y., Greco, S., Słowiński, R.: Probabilistic rough sets. In: Springer Handbook of Computational Intelligence, pp. 387–411. Springer (2015)
265.
266.
Yao, Y., Zhao, Y., Maguire, R.B.: Explanation oriented association mining using rough set theory. In: International Workshop on Rough Sets, Fuzzy Sets, Data Mining, and Granular-Soft Computing, pp. 165–172. Springer (2003)
267.
268.
Ye, D., Chen, Z., Ma, S.: A novel and better fitness evaluation for rough set based minimum attribute reduction problem. Inf. Sci. 222, 413–423 (2013)MathSciNetMATHCrossRef
269.
Yong, L., Congfu, X., Yunhe, P.: An incremental rule extracting algorithm based on pawlak reduction. In: 2004 IEEE International Conference on Systems, Man and Cybernetics, vol. 6, pp. 5964–5968. IEEE (2004)
270.
Yong, L., Wenliang, H., Yunliang, J., Zhiyong, Z.: Quick attribute reduct algorithm for neighborhood rough set model. Inf. Sci. 271, 65–81 (2014)MathSciNetMATHCrossRef
271.
Yu, H., Chu, S., Yang, D.: Autonomous knowledge-oriented clustering using decision-theoretic rough set theory. Fundamenta Informaticae 115(2–3), 141–156 (2012)MathSciNetMATH
272.
Yu, H., Liu, Z., Wang, G.: An automatic method to determine the number of clusters using decision-theoretic rough set. Int. J. Approximate Reasoning 55(1), 101–115 (2014)MathSciNetMATHCrossRef
273.
Yu, H., Su, T., Zeng, X.: A three-way decisions clustering algorithm for incomplete data. In: International Conference on Rough Sets and Knowledge Technology, pp. 765–776. Springer (2014)
274.
Yu, H., Wang, G., Lan, F.: Solving the attribute reduction problem with ant colony optimization. In: Transactions on Rough Sets XIII, pp. 240–259. Springer (2011)
275.
Yu, H., Wang, Y.: Three-way decisions method for overlapping clustering. In: International Conference on Rough Sets and Current Trends in Computing, pp. 277–286. Springer (2012)
276.
Yu, H., Wang, Y., Jiao, P.: A three-way decisions approach to density-based overlapping clustering. In: Transactions on Rough Sets XVIII, pp. 92–109. Springer (2014)
277.
Yu, H., Zhang, C., Hu, F.: An incremental clustering approach based on three-way decisions. In: International Conference on Rough Sets and Current Trends in Computing, pp. 152–159. Springer (2014)
278.
Yu, Y., Miao, D., Zhang, Z., Wang, L.: Multi-label classification using rough sets. In: International Workshop on Rough Sets, Fuzzy Sets, Data Mining, and Granular-Soft Computing, pp. 119–126. Springer (2013)
279.
Yu, Y., Pedrycz, W., Miao, D.: Multi-label classification by exploiting label correlations. Expert Syst. Appl. 41(6), 2989–3004 (2014)CrossRef
280.
Zhai, J., Zhang, S., Zhang, Y.: An extension of rough fuzzy set. J. Intell. Fuzzy Syst. (Preprint), 1–10 (2016)
281.
Zhai, J., Zhang, Y., Zhu, H.: Three-way decisions model based on tolerance rough fuzzy set. Int. J. Mach. Learn. Cybern. 1–9 (2016)
282.
Zhang, H.R., Min, F.: Three-way recommender systems based on random forests. Knowl. Based Syst. 91, 275–286 (2016)CrossRef
283.
Zhang, J., Li, T., Chen, H.: Composite rough sets. In: International Conference on Artificial Intelligence and Computational Intelligence, pp. 150–159. Springer (2012)
284.
285.
Zhang, J., Li, T., Pan, Y.: Parallel rough set based knowledge acquisition using mapreduce from big data. In: Proceedings of the 1st International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications, pp. 20–27. ACM (2012)
286.
Zhang, J., Li, T., Ruan, D., Gao, Z., Zhao, C.: A parallel method for computing rough set approximations. Inf. Sci. 194, 209–223 (2012)CrossRef
287.
Zhang, J., Li, T., Ruan, D., Liu, D.: Rough sets based matrix approaches with dynamic attribute variation in set-valued information systems. Int. J. Approximate Reasoning 53(4), 620–635 (2012)MathSciNetMATHCrossRef
288.
Zhang, L., Hu, Q., Duan, J., Wang, X.: Multi-label feature selection with fuzzy rough sets. In: International Conference on Rough Sets and Knowledge Technology, pp. 121–128. Springer (2014)
289.
Zhang, L., Li, H., Zhou, X., Huang, B., Shang, L.: Cost-sensitive sequential three-way decision for face recognition. In: International Conference on Rough Sets and Intelligent Systems Paradigms, pp. 375–383. Springer (2014)
290.
Zhang, M.L., Zhou, Z.H.: Ml-knn: A lazy learning approach to multi-label learning. Pattern Recogn. 40(7), 2038–2048 (2007)MATHCrossRef
291.
Zhang, T., Chen, L., Ma, F.: An improved algorithm of rough k-means clustering based on variable weighted distance measure. Int. J. Database Theory Appl. 7(6), 163–174 (2014)CrossRef
292.
Zhang, T., Chen, L., Ma, F.: A modified rough c-means clustering algorithm based on hybrid imbalanced measure of distance and density. Int. J. Approximate Reasoning 55(8), 1805–1818 (2014)MATHCrossRef
293.
Zhang, X., Miao, D.: Three-way weighted entropies and three-way attribute reduction. In: International Conference on Rough Sets and Knowledge Technology, pp. 707–719. Springer (2014)
294.
Zhang, Y., Zhou, Z.H.: Cost-sensitive face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 32(10), 1758–1769 (2010)CrossRef
295.
Zhao, H., Min, F., Zhu, W.: Test-cost-sensitive attribute reduction based on neighborhood rough set. In: 2011 IEEE International Conference on Granular Computing (GrC), pp. 802–806. IEEE (2011)
296.
Zhao, H., Wang, P., Hu, Q.: Cost-sensitive feature selection based on adaptive neighborhood granularity with multi-level confidence. Inf. Sci. 366, 134–149 (2016)MathSciNetCrossRef
297.
Zhao, M., Luo, K., Liao, X.X.: Rough set attribute reduction algorithm based on immune genetic algorithm. Jisuanji Gongcheng yu Yingyong (Comput. Eng. Appl.) 42(23), 171–173 (2007)
298.
Zhao, S., Chen, H., Li, C., Du, X., Sun, H.: A novel approach to building a robust fuzzy rough classifier. IEEE Trans. Fuzzy Syst. 23(4), 769–786 (2015)CrossRef
299.
Zhao, S., Tsang, E.C., Chen, D.: The model of fuzzy variable precision rough sets. IEEE Trans. Fuzzy Syst. 17(2), 451–467 (2009)CrossRef
300.
Zhao, S., Tsang, E.C., Chen, D., Wang, X.: Building a rule-based classifier–a fuzzy-rough set approach. IEEE Trans. Knowl. Data Eng. 22(5), 624–638 (2010)
301.
Zheng, Z., Wang, G., Wu, Y.: A rough set and rule tree based incremental knowledge acquisition algorithm. In: International Workshop on Rough Sets, Fuzzy Sets, Data Mining, and Granular-Soft Computing, pp. 122–129. Springer (2003)
302.
Zhong, N., Dong, J., Ohsuga, S.: Using rough sets with heuristics for feature selection. J. Intell. Inf. Syst. 16(3), 199–214 (2001)MATHCrossRef
303.
Zhou, Z.H.: Cost-sensitive learning. In: International Conference on Modeling Decisions for Artificial Intelligence, pp. 17–18. Springer (2011)
304.
Zhou, Z.H., Liu, X.Y.: Training cost-sensitive neural networks with methods addressing the class imbalance problem. IEEE Trans. Knowl. Data Eng. 18(1), 63–77 (2006)CrossRef
305.
306.
307.
308.
Zou, W., Li, T., Chen, H., Ji, X.: Approaches for incrementally updating approximations based on set-valued information systems while attribute values’ coarsening and refining. In: 2009 IEEE International Conference on Granular Computing (2009)
Metadaten
Titel
Rough Sets in Machine Learning: A Review
verfasst von
Rafael Bello
Rafael Falcon
Copyright-Jahr
2017
Buch
Thriving Rough Sets

Print ISBN: 978-3-319-54965-1

Electronic ISBN: 978-3-319-54966-8

Copyright-Jahr: 2017

DOI
https://doi.org/10.1007/978-3-319-54966-8_5