Imbalanced data classification using second-order cone programming support vector machines

Pattern Recognition

Volumn 47, Issue 5, 2014, Pages 2070-2079

  Maldonado, Sebastián ^a   López, Julio ^b  

^a UNIVERSIDAD DE LOS ANDES   (Chile)

^b UNIVERSIDAD DIEGO PORTALES   (Chile)

Author keywords

Class imbalanced data; LP SVM; SOCP SVM; Support Vector Machines

Indexed keywords

CLASS-IMBALANCED DATA; CLASSIFICATION PERFORMANCE; IMBALANCED CLASSIFICATION; IMBALANCED DATA SETS; LP-SVM; REGULARIZATION PARAMETERS; SECOND-ORDER CONE PROGRAMMING; SOCP-SVM;

BENCHMARKING; CLASSIFICATION (OF INFORMATION);

SUPPORT VECTOR MACHINES;

EID: 84893704508     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patcog.2013.11.021     Document Type: Article

References (40)

1. J. Alcalá-Fdez, L. Sánchez, S. García, M.J. del Jesus, S. Ventura, J.M. Garrell, J. Otero, C. Romero, J. Bacardit, V.M. Rivas, J.C. Fernández, and F. Herrera Keel: A software tool to assess evolutionary algorithms to data mining problems Soft Comput. 13 3 2009 307 318
2. F. Alizadeh, and D. Goldfarb Second-order cone programming Math. Progr. 95 2003 3 51
3. F. Alvarez, J. López, and H.C. Ramírez Interior proximal algorithm with variable metric for second-order cone programming applications to structural optimization and support vector machines Optim. Methods Softw. 25 6 2010 859 881
4. A. Asuncion, D.J. Newman, UCI Machine Learning Repository, 2007.
5. F. R Bach, D. Heckerman, and E. Horvitz Considering cost asymmetry in learning classifiers J. Mach. Learn. Res. 7 2006 1713 1741
6. K.P. Bennett, E.J. Bredensteiner, Duality and geometry in svm classifiers, in: Proceedings of 17th International Conference on Machine Learning, Morgan Kaufmann, 2000, pp. 57-64.
7. C. Bhattacharyya Second order cone programming formulations for feature selection J. Mach. Learn. Res. 5 2004 1417 1433
8. N.V. Chawla, L.O. Hall, K.W. Bowyer, and W.P. Kegelmeyer Smote Synthetic minority oversampling technique J. Artif. Intell. Res. 16 2002 321 357
9. N.V. Chawla, N. Japkowicz, and A. Kotcz Editorial special issue on learning from imbalanced data sets SIGKDD Explor. 6 2004 1 6
10. C. Cortes, and V. Vapnik Support-vector networks Mach. Learn. 20 1995 273 297
11. Z. Ding, Diversified ensemble classifiers for highly imbalanced data learning and their application in bioinformatics (Ph.D. thesis), Computer Science Dissertations, Paper 60, 2011.
12. T. Fawcett, and F. Provost Adaptive fraud detection Data Min. Knowl. Discov. 1 1997 291 316
13. F. Fernández-Navarro, C. Hervás-Martínez, and P.A. Gutiérrez A dynamic over-sampling procedure based on sensitivity for multi-class problems Pattern Recognit. 44 2011 1821 1833
14. H. He, and E. García Learning from imbalanced data IEEE Trans. Knowl. Data Eng. 21 2009 1263 1284
15. J. Van Hulse, T.M. Khoshgoftaar, A. Napolitano, R. Wald, Feature selection with high-dimensional imbalanced data, in: Proceedings of the IEEE International Conference on Data Mining Workshops, 2009, pp. 507-514.
16. T.M. Khoshgoftaar, J. Van Hulse, and A. Napolitano An exploration of learning when data is noisy and imbalanced Intell. Data Anal. 15 2011 215 236
17. D.A. Kumar, and V. Ravi Predicting credit card customer churn in banks using data mining Int. J. Data Anal. Tech. Strateg. 1 2008 4 28
18. G. Lanckriet, L.E. Ghaoui, C. Bhattacharyya, and M. Jordan A robust minimax approach to classification J. Mach. Learn. Res. 3 2003 555 582
19. S. Maldonado, and R. Weber A wrapper method for feature selection using support vector machines Inf. Sci. 179 2009 2208 2217
20. S. Maldonado, R. Weber, and J. Basak Kernel-penalized SVM for feature selection Inf. Sci. 181 1 2011 115 128
21. O.L. Mangasarian A finite newton method for classification Optim. Methods Softw. 17 5 2002 913 929
22. S. Nath, C. Bhattacharyya, Maximum margin classifiers with specified false positive and false negative error rates, in: Proceedings of the SIAM International Conference on Data mining, 2007.
23. X. Peng Building sparse twin support vector machine classifiers in primal space Inf. Sci. 181 18 2011 3967 3980
24. K. Pichara, and A. Soto Active learning and subspace clustering for anomaly detection Intell. Data Anal. 15 2011 151 171
25. J. Platt, Fast training of support vector machines using sequential minimal optimization, in: Advances in Kernel Methods-Support Vector Learning, MIT Press, Cambridge, MA, 1999, pp. 185-208.
26. B. Raskutti, and A. Kowalczyk Extreme rebalancing for SVMS a case study ACM SIGKDD Explor. Newslett. 6 2004 60 69 (Special Issue on Learning from Imbalanced Datasets)
27. K.B. Schebesch, R. Stecking, Data analysis, machine learning and applications, in: Using Multiple SVM Models for Unbalanced Credit Scoring Data Sets, Springer, Berlin, Heidelberg, 2008, pp. 515-522.
28. B. Schölkopf, and A.J. Smola Learning with Kernels 2002 MIT Press
29. P.K. Shivaswamy, C. Bhattacharyya, and A.J. Smola Second order cone programming approaches for handling missing and uncertain data J. Mach. Learn. Res. 7 2006 1283 1314
30. M. Sokolova, N. Japkowicz, S. Szpakowicz, Beyond accuracy, f-score and ROC: a family of discriminant measures for performance evaluation, in: Advances in Artificial Intelligence, Springer, Berlin, Heidelberg, 2006, pp. 1015-1021.
31. J.F. Sturm Using sedumi 10.2, a matlab toolbox for optimization over symmetric cones Optim. Methods Softw. 11 12 1999 625 653 (Special Issue on Interior Point Methods (CD supplement with software))
32. Y.M. Sun, M.S. Kamel, A.K.C. Wong, and Y. Wang Cost-sensitive boosting for classification of imbalanced data Pattern Recognit. 40 2007 3358 3378
33. Y. Tang, S. Krasser, D. Alperovitch, P. Judge, Spam sender detection with classification modeling on highly imbalanced mail server behavior data, in: Proceedings of the International Conference on Artificial Intelligence and Pattern Recognition, 2008, pp. 174-180.
34. D.M.J. Tax, and R. Duin Support vector data description Mach. Learn. 54 2004 45 66
35. V. Vapnik Statistical Learning Theory 1998 John Wiley and Sons
36. J. Wang, J. You, Q. Li, and Y. Xu Extract minimum positive and maximum negative features for imbalanced binary classification Pattern Recognit. 45 2012 1136 1145
37. G.M. Weiss Mining with rarity a unifying framework ACM SIGKDD Explor. Newslett. 6 2004 7 19
38. J. Weston, A. Elisseeff, G. BakIr, F. Sinz, The Spider Machine Learning Toolbox, 2005.
39. Z. Zheng, X. Wu, and R. Srihari Feature selection for text categorization on imbalanced data SIGKDD Explor. 6 2004 80 89
40. W. Zhou, L. Zhang, and L. Jiao Linear programming support vector machines Pattern Recognit. 35 2002 2927 2936