Preprocessing unbalanced data using support vector machine

Decision Support Systems

Volumn 53, Issue 1, 2012, Pages 226-233

  Farquad, M A H ^a   Bose, Indranil ^b  

^a UNIVERSITY OF HONG KONG   (Hong Kong)

^b INDIAN INSTITUTE OF MANAGEMENT CALCUTTA   (India)

Author keywords

COIL data; Hybrid method; Preprocessor; SVM; Unbalanced data

Indexed keywords

BALANCING TECHNIQUES; CLASS IMBALANCE PROBLEMS; CLASSIFICATION ALGORITHM; COIL DATA; COMPUTATIONAL INTELLIGENCE TECHNIQUES; HYBRID METHOD; LOGISTIC REGRESSIONS; MULTI LAYER PERCEPTRON; OVER SAMPLING; RANDOM FORESTS; SOCIODEMOGRAPHIC DATA; SUPPORT VECTOR MACHINE (SVM); SVM; SYNTHETIC MINORITY OVER-SAMPLING TECHNIQUES; TARGET VALUES; TRAINING DATA; UNBALANCED DATA; UNDER-SAMPLING;

ARTIFICIAL INTELLIGENCE; DECISION TREES; LOGISTICS; PROGRAM PROCESSORS;

SUPPORT VECTOR MACHINES;

EID: 84859213527     PISSN: 01679236     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.dss.2012.01.016     Document Type: Article

References (55)

1. S.M. Alladi, S.P. Santosh, V. Ravi, and U.S. Murthy Colon cancer prediction with genetic profiles using intelligent techniques Bioinformation 3 3 2008 130 133
2. R. Barandela, J.S. Sánchez, V. García, and E. Rangel Strategies for learning in class imbalance problems Pattern Recognition 36 3 2003 849 851
3. G.E.A.P.A. Batista, M.C. Monad, and A.L.C. Bazzan Improving rule induction precision for automated annotation by balancing skewed data sets Knowledge Exploration in Life Science Informatics 3303 2004 20 32
4. G.E.A.P.A. Batista, R.C. Prati, and M.C. Monard A study of the behaviour of several methods for balancing machine learning training data ACM SIGKDD Explorations: Special Issue on Imbalanced Data Sets 6 1 2004 20 29
5. C.M. Bishop Neural Networks for Pattern Recognition 1995 Oxford University Press UK
6. I. Bose, and X. Chen Hybrid models using unsupervised clustering for prediction of customer churn Journal of Organizational Computing and Electronic Commerce 19 2 2009 133 151
7. M. Bosque Understanding 99% of Artificial Neural Networks 2002 Writers Club Press
8. L. Breiman Random forests Machine Learning 45 1 2001 5 32
9. N.V. Chawla, K.W. Bowyer, L.O. Hall, and W.P. Kegelmeyer SMOTE: synthetic minority over-sampling technique Journal of Artificial Intelligence Research 16 2002 21 357
10. G. Cohen, M. Hilario, H. Sax, S. Hogonnet, and A. Geissbuhler Learning from imbalanced data in surveillance of nosocomial infection Artificial Intelligence in Medicine 37 2006 7 18
11. P. Domingos MetaCost: a general method for making classifiers cost-sensitive Proceedings of the 5th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Diego, CA, USA 1999 155 164
12. T. Eitrich, A. Kless, C. Druska, W. Meyer, and J. Grotendorst Classification of highly unbalanced cyp450 data of drugs using cost sensitive machine learning techniques Journal of Chemical Information and Modeling 47 1 2007 92 103
13. A. Estabrooks, T. Jo, and N. Japkowicz A multiple resampling method for learning from imbalanced data sets Computational Intelligence 20 1 2004 18 36
14. M.A.H. Farquad, V. Ravi, and S.B. Raju Data mining using rules extracted from SVM: an application to churn prediction in bank credit cards 12th International Conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing, December 16-18, 2009, New Delhi, India 2009 390 397
15. M.A.H. Farquad, V. Ravi, and S.B. Raju Rule extraction from Support Vector Machine using modified active learning based approach: an application to CRM R. Setchi, 14th International Conference on Knowledge-based and Intelligent Information and Engineering Systems, Part I, September 8-10, 2010, Cardiff, Wales, UK 2010 461 470
16. T. Fawcett An introduction to ROC analysis Pattern Recognition Letters 27 2006 861 874
17. T. Fawcett, and F. Provost Adaptive fraud detection Data Mining and Knowledge Discovery 1 3 1997 291 316
18. H. Guo, and H.L. Viktor Learning from imbalanced data sets with boosting and data generation: the data boosting approach SIGKDD Explorations 6 1 2004 30 39
19. X. Guo, Y. Yin, C. Dong, G. Yang, and G. Zhou On the class imbalance problem Fourth International Conference on Natural Computation, Jinan, China 2008 192 201
20. I. Guyon, J. Weston, S. Barnhill, and V.N. Vapnik Gene selection for cancer classification using support vector machines Machine Learning 46 1-3 2002 389 422
21. H. Han, W.Y. Wang, and B.H. Mao Borderline-SMOTE: a new over-sampling method in imbalanced data sets learning Proceedings of the International Conference on Intelligent Computing 2005, Part I, Hefei, China 2005 878 887
22. P.E. Hart The condensed nearest neighbor rule IEEE Transactions on Information Theory 18 1968 515 516
23. C.S. Hilas Designing an expert system for fraud detection in private telecommunications networks Expert Systems with Applications 36 9 2009 11559 11569
24. Z. Huang, H. Chen, C.-J. Hsu, W.-H. Chen, and S. Wu Credit rating analysis with support vector machines and neural networks: a market comparative study Decision Support Systems 37 4 2004 543 558
25. K. Huang, H. Yang, I. King, and M.R. Lyu Learning classifiers from imbalanced data based on biased minimax probability machine IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Washington, DC, USA 2004 558 563
26. N. Japkowicz, and S. Stephen The class imbalance problem: a systematic study Intelligent Data Analysis 6 5 2002 429 450
27. T. Jo, and N. Japkowicz Class imbalances versus small disjuncts SIGKDD Explorations 6 1 2004 40 49
28. K.J. Kim Financial time series forecasting using support vector machines Neurocomputing 55 1/2 2003 307 319
29. I. Kononenko Machine learning for medical diagnosis: history, state of the art and perspective Artificial Intelligence in Medicine 23 1 2001 89 109
30. S. Kotsiantis, D. Kanellopoulos, and P. Pintelas Handling imbalanced datasets: a review GESTS International Transactions on Computer Science and Engineering 30 1 2006 25 36
31. M. Kubat, and S. Matwin Addressing the curse of imbalanced training sets: one sided selection 14th International Conference on Machine Learning, Nashville, TN, USA 1997 179 186
32. M. Kubat, R. Holte, and S. Matwin Machine learning for the detection of oil spills in satellite radar images Machine Learning 30 2-3 2004 195 215
33. J. Laurikkala Improving identification of difficult small classes by balancing class distribution Artificial Intelligence in Medicine 2101 2001 63 66
34. W. Lee, S. Stolfo, and K. Mok Mining in a data-flow environment: Experience in network intrusion detection, 5th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 1999 San Diego CA, USA pp. 114-124
35. C.X. Ling, and C. Li Data mining for direct marketing problems and solutions Proceedings of the 4th International Conference on Knowledge Discovery and Data Mining, New York, New York, USA 1998 73 79
36. Y. Liu, N.V. Chawla, M.P. Harper, E. Shrilberg, and A. Stolcke A study in machine learning from imbalanced data for sentence boundary detection in speech Computer Speech and Language 20 4 2006 468 494
37. M. Maloof Learning when data sets are imbalanced and when costs are unequal and unknown Proceedings of the ICML 2003 Workshop on Learning from Imbalanced Datasets, Washington, DC, USA 2003 73 80
38. M. Mannino, Y. Yang, and Y. Rhu Classification algorithm sensitivity to training data with non representative attribute noise Decision Support Systems 46 3 2009 743 751
39. M.C. Monard, and G.E.A.P.A. Batista Learning with skewed class distribution Advances in Logic, Artificial Intelligence and Robotics 2002 173 180
40. P. Ravisankar, V. Ravi, G.R. Rao, and I. Bose Detection of financial statement fraud and feature selection using data mining techniques Decision Support Systems 50 2 2011 491 500
41. D. Sanchez, M.A. Vila, L. Cerda, and J.M. Serrano Association rules applied to credit card fraud detection Expert Systems with Applications 36 2 2009 3630 3640
42. F. Sebastiani Machine learning in automated text categorization ACM Computing Surveys 34 1 2002 1 47
43. D. Stoneking Improving the manufacturability of electronic designs IEEE Spectrum 36 6 1999 70 76
44. A. Sun, E.-P. Lim, and Y. Liu On strategies for imbalanced text classification using SVM: a comparative study Decision Support Systems 48 1 2009 191 201
45. B.G. Tabachnick, and L.S. Fidell Logistic regression Retrieved on October 10, 2011, from http://userwww.sfsu.edu/~efc/classes/biol710/logistic/ logisticreg.htm 1996
46. CoIL Challenge 2000: the insurance company case P. van der Putten, and M. van Someren Leiden Institute of Advanced Computer Science Technical Report 2000-09 June 22, 2000
47. V.N. Vapnik The Nature of Statistical Learning Theory 1995 Springer-Verlag New York, USA
48. V.N. Vapnik The Nature of Statistical Learning Theory 2nd edition 1998 Springer-Verlag New York, USA
49. S. Visa, and A. Ralescu Issues in mining imbalanced data sets - a review paper Proceedings of the 16th Midwest Artificial Intelligence and Cognitive Science Conference, Dayton, OH, USA 2005 67 73
50. G.M. Weiss Learning with rare cases and small disjuncts Proceedings of the 12th International Conference on Machine Learning, Tahoe City, CA, USA 1995 558 565
51. G.M. Weiss Mining with rarity: a unifying framework SIGKDD Explorations 6 1 2004 7 19
52. D.L. Wilson Asymptotic properties of nearest neighbor rules using edited data IEEE Transactions on Systems, Man, and Cybernetics 2 1972 408 420
53. G. Wu, and E. Chang Class-boundary Alignment for Imbalanced Dataset Learning, ICML 2003 Workshop on Learning from Imbalanced Data Sets, Washington, DC, USA 2003
54. X. Wu, V. Kumar, J.R. Quinlan, J. Ghosh, Q. Yang, H. Motoda, G.J. McLachlan, A. Ng, B. Liu, P.S. Yu, Z.-H. Zhou, M. Steinbach, D.J. Hand, and D. Steinberg Top 10 algorithms in data mining Knowledge and Information Systems 14 1 2008 1 37
55. C. Yang Web user behavioral profiling for user identification Decision Support Systems 49 3 2010 261 271