Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Bücher
Zeitschriften
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
MTZ-Fachkongress

Antriebe und Energiesysteme von morgen


Austausch von Automobil- und Energiewirtschaft

Am 14./15. Mai geht es in Chemnitz um die Mobilitätswende durch Elektro- und Wasserstofffahrzeuge.
Erweiterte Suche
Anmelden
nach oben
Neural Computing and Applications
Erschienen in: Neural Computing and Applications 10/2019

02.04.2018 | Original Article

CBR-PSO: cost-based rough particle swarm optimization approach for high-dimensional imbalanced problems

verfasst von: Emel Kızılkaya Aydogan, Mihrimah Ozmen, Yılmaz Delice

Erschienen in: Neural Computing and Applications | Ausgabe 10/2019

Einloggen

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

search-config
loading …

Abstract

Datasets, which have a considerably larger number of attributes compared to samples, face a serious classification challenge. This issue becomes even harder when such high-dimensional datasets are also imbalanced. Recently, such datasets have attracted the interest of both industry and academia and thereby have become a very attractive research area. In this paper, a new cost-sensitive classification method, the CBR-PSO, is presented for such high-dimensional datasets with different imbalance ratios and number of classes. The CBR-PSO is based on particle swarm optimization and rough set theory. The robustness of the algorithm is based on the simultaneously applying attribute reduction and classification; in addition, these two stages are also sensitive to misclassification cost. Algorithm efficiency is examined in publicly available datasets and compared to well-known attribute reduction and cost-sensitive classification algorithms. The statistical analysis and experiments showed that the CBR-PSO can be better than or comparable to the other algorithms, in terms of MAUC values.
Vorheriger Artikel Fuzzy logic controller-based MPPT for hybrid photo-voltaic/wind/fuel cell power system
Nächster Artikel Synchronization of single-degree-of-freedom oscillators via neural network based on fixed-time terminal sliding mode control scheme

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

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

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+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
Anhänge
Nur mit Berechtigung zugänglich
Literatur
1.
Yang Q, Wu X (2006) 10 challenging problems in data mining research. Int J Inf Technol Decis Mak 5(04):597–604CrossRef
2.
López V, Fernández A, García S, Palade V, Herrera F (2013) An insight into classification with imbalanced data: empirical results and current trends on using data intrinsic characteristics. Inf Sci 250:113–141CrossRef
3.
Hu Q, Zhao H, Xie Z, Yu D (2007) Consistency based attribute reduction. In: Pacific-Asia conference on knowledge discovery and data mining. Springer, Berlin, pp 96–107
4.
Min F, He H, Qian Y, Zhu W (2011) Test-cost-sensitive attribute reduction. Inf Sci 181(22):4928–4942CrossRef
5.
6.
Wang C, Wu C, Chen D (2008) A systematic study on attribute reduction with rough sets based on general binary relations. Inf Sci 178(9):2237–2261MathSciNetMATHCrossRef
7.
8.
Zhang WX, Mi JS, Wu WZ (2003) Approaches to knowledge reductions in inconsistent systems. Int J Intell Syst 18(9):989–1000MATHCrossRef
9.
Zhao Y, Wong SM, Yao Y (2011) A note on attribute reduction in the decision-theoretic rough set model. In: Transactions on rough sets XIII. Springer, Berlin, pp 260–275
10.
Zhou X, Li H (2009) A multi-view decision model based on decision-theoretic rough set. In: International conference on rough sets and knowledge technology. Springer, Berlin, pp. 650–657
11.
Jia X, Liao W, Tang Z, Shang L (2013) Minimum cost attribute reduction in decision-theoretic rough set models. Inf Sci 219:151–167MathSciNetMATHCrossRef
12.
Aydogan EK, Karaoglan I, Pardalos PM (2012) hGA: hybrid genetic algorithm in fuzzy rule-based classification systems for high-dimensional problems. Appl Soft Comput 12(2):800–806CrossRef
13.
Berlanga FJ, Rivera AJ, del Jesús MJ, Herrera F (2010) GP-COACH: genetic programming-based learning of compact and accurate fuzzy rule-based classification systems for high-dimensional problems. Inf Sci 180(8):1183–1200CrossRef
14.
15.
16.
Rifkin R, Klautau A (2004) In defense of one-vs-all classification. J Mach Learn Res 5:101–141MathSciNetMATH
17.
FernáNdez A, LóPez V, Galar M, Del Jesus MJ, Herrera F (2013) Analysing the classification of imbalanced data-sets with multiple classes: binarization techniques and ad-hoc approaches. Knowl Based Syst 42:97–110CrossRef
18.
Thanathamathee P, Lursinsap C (2013) Handling imbalanced datasets with synthetic boundary data generation using bootstrap re-sampling and AdaBoost techniques. Pattern Recognit Lett 34(12):1339–1347CrossRef
19.
McCarthy K, Zabar B, Weiss G (2005) Does cost-sensitive learning beat sampling for classifying rare classes?. In: Proceedings of the 1st international workshop on Utility-based data mining. ACM, pp 69–77
20.
Liu W, Chawla S, Cieslak DA, Chawla NV (2010) A robust decision tree algorithm for imbalanced datasets. In: SDM, vol 10, pp 766–777
21.
Liu J, Hu Q, Yu D (2008) A comparative study on rough set based class-imbalance learning. Knowl Based Syst 21(8):753–763CrossRef
22.
Sinha S, Singh TN, Singh VK, Verma AK (2010) Epoch determination for neural network by self-organized map (SOM). Comput Geosci 14(1):199–206MATHCrossRef
23.
Hong X, Chen S, Harris CJ (2007) A kernel-based two-class classifier for imbalanced datasets. IEEE Trans Neural Netw 18(1):28–41CrossRef
24.
Galar M, Fernandez A, Barrenechea E, Bustince H, Herrera F (2012) A review on ensembles for the class-imbalance problem: bagging-, boosting-, and hybrid-based approaches. IEEE Trans Syst Man Cybern Part C (Appl Rev) 42(4):463–484CrossRef
25.
Ertekin S, Huang J, Giles CL (2007) Active learning for class-imbalance problem. In: Proceedings of the 30th annual international ACM SIGIR conference on research and development in information retrieval. ACM, pp 823–824
26.
Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP (2002) SMOTE: synthetic minority over-sampling technique. J Artif Intell Res 16:321–357MATHCrossRef
27.
Galar M, Fernández A, Barrenechea E, Bustince H, Herrera F (2011) An overview of ensemble methods for binary classifiers in multi-class problems: experimental study on one-vs-one and one-vs-all schemes. Pattern Recognit 44(8):1761–1776CrossRef
28.
Elkan C, Noto K (2008) Learning classifiers from only positive and unlabeled data. In: Proceedings of the 14th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 213–220
29.
Chawla NV, Lazarevic A, Hall LO, Bowyer KW (2003) SMOTEBoost: improving prediction of the minority class in boosting. In: European conference on principles of data mining and knowledge discovery. Springer, Berlin, pp 107–119
30.
Rout N, Mishra D, Mallick MK (2018) Handling imbalanced data: a survey. In: International proceedings on advances in soft computing, intelligent systems and applications. Springer, Singapore, pp 431–443
31.
Domingos P (1999) Metacost: a general method for making classifiers cost-sensitive. In: Proceedings of the fifth ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 155–164
32.
Singh TN, Verma AK (2012) Comparative analysis of intelligent algorithms to correlate strength and petrographic properties of some schistose rocks. Eng Comput 28(1):1–12CrossRef
33.
Fan W, Stolfo SJ, Zhang J, Chan PK (1999) AdaCost: misclassification cost-sensitive boosting. In: Icml, pp 97–105
34.
Joshi MV, Kumar V, Agarwal RC (2001) Evaluating boosting algorithms to classify rare classes: comparison and improvements. In: Proceedings IEEE international conference on data mining, 2001. ICDM 2001. IEEE, pp 257–264
35.
Hu S, Liang Y, Ma L, He Y (2009) MSMOTE: improving classification performance when training data is imbalanced. In Proceedings of the 2009 Second international workshop on computer science and engineering, vol 2, pp 13–17
36.
Han H, Wang WY, Mao BH (2005) Borderline-SMOTE: a new over-sampling method in imbalanced datasets learning. In: International conference on intelligent computing. Springer, Berlin, pp 878–887
37.
He H, Bai Y, Garcia EA, Li S (2008) ADASYN: adaptive synthetic sampling approach for imbalanced learning. In: 2008 IEEE international joint conference on neural networks (IEEE world congress on computational intelligence). IEEE, pp 1322–1328
38.
Kubat M, Matwin S (1997). Addressing the curse of imbalanced training sets: one-sided selection. In: ICML, vol 97, pp 179–186
39.
Batista GE, Prati RC, Monard MC (2004) A study of the behavior of several methods for balancing machine learning training data. ACM Sigkdd Explor Newsl 6(1):20–29CrossRef
40.
Stefanowski J, Wilk S (2008) Selective pre-processing of imbalanced data for improving classification performance. In: International conference on data warehousing and knowledge discovery. Springer, Berlin, pp 283–292
41.
Madhubabu N, Singh PK, Kainthola A, Mahanta B, Tripathy A, Singh TN (2016) Prediction of compressive strength and elastic modulus of carbonate rocks. Measurement 88:202–213CrossRef
42.
Elkan C (2001b) The foundations of cost-sensitive learning. In: International joint conference on artificial intelligence, vol 17, no. 1. Lawrence Erlbaum Associates Ltd, pp 973–978
43.
Ting KM (2002) An instance-weighting method to induce cost-sensitive trees. IEEE Trans Knowl Data Eng 14(3):659–665MathSciNetCrossRef
44.
Drummond C, Holte RC (2003) C4. 5, class-imbalance, and cost sensitivity: why under-sampling beats over-sampling. In: Workshop on learning from imbalanced datasets II, vol 11
45.
Maloof MA (2003) Learning when datasets are imbalanced and when costs are unequal and unknown. In: ICML-2003 workshop on learning from imbalanced datasets II, vol 2, pp 2–1
46.
Zhou ZH, Liu XY (2006) Training cost-sensitive neural networks with methods addressing the class-imbalance problem. IEEE Trans Knowl Data Eng 18(1):63–77MathSciNetCrossRef
47.
Allwein EL, Schapire RE, Singer Y (2000) Reducing multiclass to binary: a unifying approach for margin classifiers. J Mach Learn Res 1(Dec):113–141MathSciNetMATH
48.
Ramakrishnan D, Singh TN, Purwar N, Barde KS, Gulati A, Gupta S (2008) Artificial neural network and liquefaction susceptibility assessment: a case study using the 2001 Bhuj earthquake data, Gujarat, India. Comput Geosci 12(4):491–501CrossRef
49.
Haixiang G, Xiuwu L, Kejun Z, Chang D, Yanhui G (2011) Optimizing reservoir features in oil exploration management based on fusion of soft computing. Appl Soft Comput 11(1):1144–1155CrossRef
50.
51.
Hand DJ, Till RJ (2001) A simple generalisation of the area under the ROC curve for multiple class classification problems. Mach Learn 45(2):171–186MATHCrossRef
52.
Anil Kumar D, Ravi V (2008) Predicting credit card customer churn in banks using data mining. Int J Data Anal Techn Strat 1(1):4–28CrossRef
53.
Ramaswamy S, Ross KN, Lander ES, Golub TR (2003) A molecular signature of metastasis in primary solid tumors. Nat Genet 33(1):49–54CrossRef
54.
Horng JT, Wu LC, Liu BJ, Kuo JL, Kuo WH, Zhang JJ (2009) An expert system to classify microarray gene expression data using gene selection by decision tree. Expert Syst Appl 36(5):9072–9081CrossRef
55.
Zheng Z, Wu X, Srihari R (2004) Feature selection for text categorization on imbalanced data. ACM Sigkdd Explor Newsl 6(1):80–89CrossRef
56.
Shang W, Huang H, Zhu H, Lin Y, Qu Y, Wang Z (2007) A novel feature selection algorithm for text categorization. Expert Syst Appl 33(1):1–5CrossRef
57.
Tan S (2008) An improved centroid classifier for text categorization. Expert Syst Appl 35(1):279–285CrossRef
58.
Park BJ, Oh SK, Pedrycz W (2013) The design of polynomial function-based neural network predictors for detection of software defects. Inf Sci 229:40–57MathSciNetMATHCrossRef
59.
Iizuka N, Oka M, Yamada-Okabe H, Nishida M, Maeda Y, Mori N, Takao T, Tamesa T, Tangoku A, Tabuchi H, Hamada K (2003) Oligonucleotide microarray for prediction of early intrahepatic recurrence of hepatocellular carcinoma after curative resection. The lancet 361(9361):923–929CrossRef
60.
Guyon I, Elisseeff A (2003) An introduction to variable and feature selection. J Mach Learn Res 3(Mar):1157–1182MATH
61.
Hamid A, Dwivedi US, Singh TN, Gopi Kishore M, Mahmood M, Singh H, Tandon V, Singh PB (2003) Artificial neural networks in predicting optimum renal stone fragmentation by extracorporeal shock wave lithotripsy: a preliminary study. BJU Int 91(9):821–824CrossRef
62.
Pawlak, Z. (1991). Rough sets: theoretical aspects of reasoning about data, system theory. Knowl Eng Probl Solving, vol 9
63.
Lurie JD, Sox HC (1999) Principles of medical decision making. Spine 24(5):493–498CrossRef
64.
Sherif M, Hovland CI (1961) Social judgment: assimilation and contrast effects in communication and attitude change. YALE University Press, New Haven, CT
65.
Ali K, Manganaris S, Srikant R (1997) Partial classification using association rules. In: KDD, vol 97, pp p115–118)
66.
Pawlak Z, Wong SKM, Ziarko W (1988) Rough sets: probabilistic versus deterministic approach. Int J Man Mach Stud 29(1):81–95MATHCrossRef
67.
Yao YY (1990) Wong. SKM, Lingras. P.: A decision-theoretic rough set model. In: Proceeding of ISMIS, pp 17–25
68.
Duda RO, Hart PE (1973) Pattern classification and scene analysis, vol 3. Wiley, New YorkMATH
69.
70.
Yang Y, Webb GI (2009) Discretization for naive-Bayes learning: managing discretization bias and variance. Mach Learn 74(1):39–74CrossRef
71.
Sousa T, Silva A, Neves A (2004) Particle swarm based data mining algorithms for classification tasks. Parallel Comput 30(5):767–783CrossRef
72.
De Falco I, Della Cioppa A, Tarantino E (2007) Facing classification problems with particle swarm optimization. Appl Soft Comput 7(3):652–658CrossRef
73.
Kennedy J, Eberhart RC (1997). A discrete binary version of the particle swarm algorithm. In: Systems, man, and cybernetics, 1997. Computational cybernetics and simulation., 1997 IEEE international conference on, vol 5. IEEE, pp 4104–4108
74.
Taşgetiren MF, Liang YC (2003) A binary particle swarm optimization algorithm for lot sizing problem. J Econ Soc Res 5(2):1–20
75.
76.
77.
Nutt CL, Mani DR, Betensky RA, Tamayo P, Cairncross JG, Ladd C, Pohl U, Hartmann C, McLaughlin ME, Batchelor TT, Black PM (2003) Gene expression-based classification of malignant gliomas correlates better with survival than histological classification. Can Res 63(7):1602–1607
78.
Quinlan JR (1993) C4.5: Programs for machine learning. Morgan Kauffman, Burlington
79.
Duda RO, Hart PE, Stork DG (2001) Pattern classification, 2nd edn. John Wiley, New YorkMATH
80.
McLachlan GJ (2004) Discriminant analysis and statistical pattern recognition. Wiley, HobokenMATH
81.
82.
Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten IH (2009) The WEKA data mining software: an update. SIGKDD Explor 11(1):10–18CrossRef
83.
Seiffert C, Khoshgoftaar TM, Van Hulse J, Napolitano A (2010) RUSBoost: a hybrid approach to alleviating class-imbalance. IEEE Trans Syst Man Cybern A 40(1):185–197CrossRef
84.
Metadaten
Titel
CBR-PSO: cost-based rough particle swarm optimization approach for high-dimensional imbalanced problems
verfasst von
Emel Kızılkaya Aydogan
Mihrimah Ozmen
Yılmaz Delice
Publikationsdatum
02.04.2018
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 10/2019
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-018-3469-2

Weitere Artikel der Ausgabe 10/2019

Neural Computing and Applications 10/2019 Zur Ausgabe

Original Article

Development of a monopoly pricing model for diffusion maximization in fuzzy weighted social networks with negative externalities of heterogeneous nodes using a case study

Original Article

Nature-inspired heuristic paradigms for parameter estimation of control autoregressive moving average systems

Original Article

A novel double incremental learning algorithm for time series prediction

Original Article

Modeling forces between the probe of atomic microscope and the scanning surface

Original Article

Hopfield network-based approach to detect seam-carved images and identify tampered regions

Original Article

Fuzzy logic-based performance improvement on MAC layer in wireless local area networks

Premium Partner

    Bildnachweise