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Neural Computing and Applications

Erschienen in:

01.03.2011 | Original Article

Imbalanced classification using support vector machine ensemble

verfasst von: Jiang Tian, Hong Gu, Wenqi Liu

Erschienen in: Neural Computing and Applications | Ausgabe 2/2011

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Abstract

Imbalanced data sets often have detrimental effects on the performance of a conventional support vector machine (SVM). To solve this problem, we adopt both strategies of modifying the data distribution and adjusting the classifier. Both minority and majority classes are resampled to increase the generalization ability. For minority class, an one-class support vector machine model combined with synthetic minority oversampling technique is used to oversample the support vector instances. For majority class, we propose a new method to decompose the majority class into clusters and remove two clusters using a distance measure to lessen the effect of outliers. The remaining clusters are used to build an SVM ensemble with the oversampled minority patterns, the SVM ensemble can achieve better performance by considering potentially suboptimal solutions. Experimental results on benchmark data sets are provided to illustrate the effectiveness of the proposed method.

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Chawla NV, Japkowicz N, Kotcz A (2004) Editorial: special issue on learning from imbalanced data sets. SIGKDD Explor 6(1):1–6CrossRef

Weiss GM (2004) Mining with rarity: a unifying framework. SIGKDD Explor 6(1):7–19CrossRef

Vapnik VN (2000) The nature of statistical learning theory. Springer, New YorkMATH

Zhao XM, Li X, Chen L, Aihara K (2008) Protein classification with imbalanced data. Proteins 70(4):1125–1132CrossRef

Wu MR, Ye JP (2009) A small sphere and large margin approach for novelty detection using training data with outliers. IEEE Trans Pattern Anal Mach Intell 31(11):2088–2092CrossRefMathSciNet

Li X, Wang L, Sung E (2008) Adaboost with svm-based component classifiers. Eng Appl Artif Intell 21(5):785–795CrossRef

Zhou ZH, Liu XY (2006) Training cost-sensitive neural networks with methods addressing the class imbalance problem. IEEE Trans Knowl Data Eng 18(1):63–77CrossRef

Sun Y, Kamel MS, Wong AKC, Wang Y (2007) Cost-sensitive boosting for classification of imbalanced data. Pattern Recognit 40(12):3358–3378MATHCrossRef

Gardner AB, Krieger AM, Vachtsevanos G, Litt B (2006) One-class novelty detection for seizure analysis from intracranial eeg. J Mach Learn Res 7(7):1025–1044MathSciNet

10.

Giacinto G, Perdisci R, Del Rio M, Roli F (2008) Intrusion detection in computer networks by a modular ensemble of one-class classifiers. Inf Fusion 9(1):69–82CrossRef

11.

Liao TW (2008) Classification of weld flaws with imbalanced class data. Expert Syst Appl 35(3):1041–1052CrossRef

12.

Guo H, Viktor H (2004) Learning from imbalanced data sets with boosting and data generation: the databoost-im approach. SIGKDD Explor 6(1):30–39CrossRef

13.

Yen SJ, Lee YS (2009) Cluster-based under-sampling approaches for imbalanced data distributions. Expert Syst Appl 36(3):5718–5727CrossRefMathSciNet

14.

Kubat M, Matwin S (1997) Addressing the curse of imbalanced training sets: one-sided selection. In: Proceedings of the 14th international conference on machine learning, Nashville, pp 179–186

15.

Wilson DR, Martinez TR (2000) Reduction techniques for instance-based learning algorithms. Mach Learn 38(3):257–286MATHCrossRef

16.

Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP (2002) Smote: Synthetic minority over-sampling technique. J Artif Intell Res 16(6):321–357MATH

17.

Liu Y, An A, Huang X (2006) Boosting prediction accuracy on imbalanced datasets with svm ensembles. In: 10th Pacific-Asia conference on advances in knowledge discovery and data mining. Springer, Singapore, pp 107–118

18.

Wang HY (2008) Combination approach of smote and biased-svm for imbalanced datasets. In: Proceedings of the international joint conference on neural networks. Institute of Electrical and Electronics Engineers Inc., Hong Kong, pp 228–231

19.

Yoon K, Kwek S (2007) A data reduction approach for resolving the imbalanced data issue in functional genomics. Neural Comput Appl 16(3):295–306CrossRef

20.

Akbani R, Kwek S, Japkowicz N (2004) Applying support vector machines to imbalanced datasets. In: Proceedings of the 15th European conference on machine learning. Springer, Pisa, pp 39–50

21.

Wu TF, Lin CJ, Weng RC (2004) Probability estimates for multi-class classification by pairwise coupling. J Mach Learn Res 5(5):975–1005MathSciNet

22.

Scholkopf B, Platt JC, Shawe-Taylor J, Smola AJ, Williamson RC (2001) Estimating the support of a high-dimensional distribution. Neural Comput 13(7):1443–1471CrossRef

23.

Lloyd SP (1982) Least-squares quantization in pcm. IEEE Trans Inf Theory 28(2):129–137MATHCrossRefMathSciNet

24.

Chang CC, Lin CJ (2001) Libsvm: a library for support vector machines. Software available at http://www.csie.ntu.edu.tw/cjlin/libsvm

25.

Witten IH, Frank E (2005) Data mining: practical machine learning tools and techniques. Morgan Kaufmann Publisers, San FransiscoMATH

26.

Kim HC, Pang S, Je HM, Kim D, Bang SY (2003) Constructing support vector machine ensemble. Pattern Recognit 36(12):2757–2767MATHCrossRef

27.

Breiman L (1996) Bagging predictors. Mach Learn 24(2):123–140MATHMathSciNet

28.

Freund Y, Schapire RE (1996) Experiments with a new boosting algorithm. In: Proceedings of the thirteenth international conference on machine learning. Morgan Kauffman, San Francisco, pp 148–156

29.

Webb GI (2000) Multiboosting: a technique for combining boosting and wagging. Mach Learn 40(2):159–196CrossRef

Titel: Imbalanced classification using support vector machine ensemble
verfasst von: Jiang Tian
Hong Gu
Wenqi Liu
Publikationsdatum: 01.03.2011
Verlag: Springer-Verlag
Erschienen in: Neural Computing and Applications / Ausgabe 2/2011
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-010-0349-9

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