A novel framework for class imbalance learning using intelligent under-sampling

Progress in Artificial Intelligence

Volumn 2, Issue 1, 2013, Pages 73-84

  Naganjaneyulu, Satuluri ^a,b   Kuppa, Mrithyumjaya Rao ^c  

^a LAKIREDDY BALI REDDY COLLEGE OF ENGINEERING   (India)

^b ACHARYA NAGARJUNA UNIVERSITY   (India)

^c VAAGDEVI COLLEGE OF ENGINEERING   (India)

Author keywords

CILIUS; Class imbalance; Classification; Weighted sampling

Indexed keywords

BOTTLES; CLASSIFICATION (OF INFORMATION); LEARNING SYSTEMS; NEURAL NETWORKS; TREES (MATHEMATICS);

AREA UNDER THE ROC CURVE; CILIUS; CLASS IMBALANCE; CLASS IMBALANCE LEARNING; DATA MINING AND KNOWLEDGE DISCOVERY; EVALUATION METRICS; RESAMPLING ALGORITHMS; SKEWED TRAINING DATUM;

DATA MINING;

ALGORITHMS; BOTTLES; CLASSIFICATION; MATHEMATICS; NEURAL NETWORKS; SAMPLING; TREES;

EID: 84900488591     PISSN: 21926352     EISSN: 21926360     Source Type: Journal    
DOI: 10.1007/s13748-012-0038-2     Document Type: Article

References (51)

1. Wu, J., Brubaker, S. C., Mullin, M. D., Rehg, J. M.: Fast asymmetric learning for cascade face detection. IEEE Trans. Pattern Anal. Mach. Intell. 30(3), 369-382 (2008).
2. Chawla, N. V., Japkowicz, N., Kotcz, A. (eds.): Proceedings of ICML Workshop Learn. Imbalanced Data Sets (2003).
3. Japkowicz, N. (ed.) Proceedings of AAAI Workshop Learn. Imbalanced Data Sets (2000).
4. Weiss, G. M.: Mining with rarity: a unifying framework. ACM SIGKDD Explor. Newslett. 6(1), 7-19 (2004).
5. Chawla, N. V., Japkowicz, N., Kolcz, A. (eds.): Special issue learning imbalanced datasets. SIGKDD Explor. Newslett. 6(1) (2004).
6. Lu, W.-Z., Wang, D.: Ground-level ozone prediction by support vector machine approach with a cost-sensitive classification scheme. Sci. Total. Environ. 395(2-3), 109-116 (2008).
7. Huang, Y.-M., Hung, C.-M., Jiau, H. C.: Evaluation of neural networks and data mining methods on a credit assessment task for class imbalance problem. Nonlinear Anal. R. World Appl. 7(4), 720-747 (2006).
8. Cieslak, D., Chawla, N., Striegel, A.: Combating imbalance in network intrusion datasets. In: IEEE International Confernce on Granular Computation, pp. 732-737 (2006).
9. Mazurowski, M. A., Habas, P. A., Zurada, J. M., Lo, J. Y., Baker, J. A., Tourassi, G. D.: Training neural network classifiers for medical decision making: the effects of imbalanced datasets on classification performance. Neural Netw. 21(2-3), 427-436 (2008).
10. Freitas, A., Costa-Pereira, A., Brazdil, P.: Cost-sensitive decision trees applied to medical data. In: Song, I., Eder, J., Nguyen, T. (eds.) Data Warehousing Knowl. Discov. (Lecture Notes Series in Computer Science) (2007).
11. Kiliç, K., Türksen, I. B.: Comparison of different strategies of utilizing fuzzy clustering in structure identification. Inf. Sci. 177(23), 5153-5162 (2007).
12. Celebi, M. E., Kingravi, H. A., Uddin, B., Iyatomi, H., Aslandogan, Y. A., Stoecker, W. V., Moss, R. H.: A methodological approach to the classification of dermoscopy images. Comput. Med. Imag. Graph. 31(6), 362-373 (2007).
13. X. Peng and I. King, "Robust BMPM training based on second-order cone programming and its application in medical diagnosis", Neural Netw., vol. 21, no. 2-3, pp. 450-457, 2008. Berlin/Heidelberg, Germany: Springer, vol. 4654, pp. 303-312, (2007).
14. Batuwita, R., Palade, V.: FSVM-CIL: fuzzy support vector machines for class imbalance learning. IEEE Trans. Fuzzy Syst. 18(3), 558-571 (2010).
15. Japkowicz, N., Stephen, S.: The class imbalance problem: a systematic study. Intell. Data Anal. 6, 429-450 (2002).
16. Kubat, M., Matwin, S.: Addressing the curse of imbalanced training sets: one-sided selection. In: Proceedings of the 14th International Conference on Machine Learning, pp. 179-186 (1997).
17. Batista, G. E. A. P. A., Prati, R. C., Monard, M. C.: A Study of the Behavior of Several Methods for Balancing Machine Learning Training Data, SIGKDD Explorations, vol. 6, pp. 20-29 (2004).
18. Cieslak, D., Chawla, N.: Learning decision trees for unbalanced data. In: Machine Learning and Knowledge Discovery in Databases. Springer, Berlin, pp. 241-256 (2008).
19. Weiss, G.: Mining with rarity: a unifying framework. SIGKDD Explor. Newslett. 6(1), 7-19 (2004).
20. Chawla, N., Bowyer, K., Kegelmeyer, P.: SMOTE: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321-357 (2002).
21. Zhang, J., Mani, I.: KNN approach to unbalanced data distributions: a case study involving information extraction. In: Proceedings of the International Conference on Machine Learning, Workshop: Learning Imbalanced Data Sets, Washington, DC, pp. 42-48 (2003).
22. Jo, T., Japkowicz, N.: Class imbalances versus small disjuncts. ACM SIGKDD Explor. Newslett. 6(1), 40-49 (2004).
23. Zou, S., Huang, Y., Wang, Y., Wang, J., Zhou, C.: SVM learning from imbalanced data by GA sampling for protein domain prediction. In: Proceedings of the 9th International Conference on Young Computer Scientists, Hunan, pp. 982-987 (2008).
24. Wang, J., You, J., Li, Q., Xu, Y.: Extract minimum positive and maximum negative features for imbalanced binary classification. Pattern Recognit. 45, 1136-1145 (2012).
25. Brown, I., Mues, C.: An experimental comparison of classification algorithms for imbalanced credit scoring data sets. Expert Syst. Appl. 39, 3446-3453 (2012).
26. Garcì, S., Derrac, J., Triguero, I., Carmona, C. J., Herrera, F.: Evolutionary-based selection of generalized instances for imbalanced classification. Knowl. Based Syst. 25, 3-12 (2012).
27. Xiao, J., Xie, L., He, C., Jiang, X.: Dynamic classifier ensemble model for customer classification with imbalanced class distribution. Expert Syst. Appl. 39, 3668-3675 (2012).
28. López, V., Fernández, A., Moreno-Torres, J. G., Herrera, F.: Analysis of preprocessing vs. cost-sensitive learning for imbalanced classification. Open problems on intrinsic data characteristics. Expert Syst. Appl. 39, 6585-6608 (2012).
29. Yong, Y.: The research of imbalanced data set of sample sampling method based on K-means cluster and genetic algorithm. Energy Procedia 17, 164-170 (2012).
30. Seiffert, C., Khoshgoftaar, T. M., Hulse, J. V., Napolitano, A.: RUSBoost: a hybrid approach to alleviating class imbalance. IEEE Trans. Syst. Man Cybern. Part A Syst. Hum. 40(1), 185 (2010).
31. Garcia, V., Sanchez, J. S., Mollineda, R. A.: On the effectiveness of preprocessing methods when dealing with different levels of class imbalance. Knowl. Based Syst. 25, 13-21 (2012).
32. Pérez-Godoy, M. D., Fernández, A., Rivera, A. J., del Jesus, M. J.: Analysis of an evolutionary RBFN design algorithm, CO2RBFN, for imbalanced data sets. Pattern Recognit. Lett. 31, 2375-2388 (2010).
33. Li, D. C., Liu, C. W., Hu, S. C.: A learning method for the class imbalance problem with medical data sets. Comput. Biol. Med. 40, 509-518 (2010).
34. Che, E., Lin, Y., Xiong, H., Luo, Q., Ma, H.: Exploiting probabilistic topic models to improve text categorization under class imbalance. Inf. Process. Manag. 47, 202-214 (2011).
35. Fernández, A., del Jesus, M. J., Herrera, F.: On the 2-tuples based genetic tuning performance for fuzzy rule based classification systems in imbalanced data-sets. Inf. Sci. 180, 1268-1291 (2010).
36. Chi, Z., Yan, H., Pham, T.: Fuzzy Algorithms with Applications to Image Processing and Pattern Recognition. World Scientific (1996).
37. Ishibuchi, H., Yamamoto, T., Nakashima, T.: Hybridization of fuzzy GBML approaches for pattern classification problems. IEEE Trans. Syst. Man Cybern. B 35(2), 359-365 (2005).
38. Burez, J., van den Poel, D.: Handling class imbalance in customer churn prediction. Expert Syst. Appl. 36, 4626-4636 (2009).
39. Hsu, C. C., Wang, K. S., Chang, S. H.: Bayesian decision theory for support vector machines: imbalance measurement and feature optimization. Expert Syst. Appl. 38, 4698-4704 (2011).
40. Fernández, A., del Jesus, M. J., Herrera, F.: On the influence of an adaptive inference system in fuzzy rule based classification systems for imbalanced data-sets. Expert Syst. Appl. 36, 9805-9812 (2009).
41. Malof, J. M., Mazurowski, M. A., Tourassi, G. D.: The effect of class imbalance on case selection for case-based classifiers: an empirical study in the context of medical decision support. Neural Netw. 25, 141-145 (2012).
42. Hall, M. A.: Correlation-based Feature Subset Selection for Machine Learning. PhD Thesis, Hamilton (1998).
43. Quinlan, J. R.: C4. 5: Programs for Machine Learning, 1st edn. Morgan Kaufmann Publishers, San Mateo (1993).
44. Rumelhart, D. E., Hinton, Geoffrey E., Williams, Ronald J., Learning representations by back-propagating errors. Nature 323(6088), 533-536 (1986). doi: 10. 1038/323533a0.
45. Asuncion, A., Newmann, D.: UCI Repository of Machine Learning Database (School of Information and Computer Science) University of California, Irvine (2007, online). http://www. ics. uci. edu/~mlearn/MLRepository. html.
46. Fernández, A., García, S., del Jesus, M. J., Herrera, F.: A study on the use of the fuzzy reasoning method based on the winning rule vs. voting procedure for classification with imbalanced data sets. In: Ninth International Work-conference on Artificial Neural Networks (IWANN07). Lecture Notes on Computer Science, vol. 4507, pp. 375-382. Springer, Berlin (2007).
47. Fernández, A., García, S., del Jesus, M. J., Herrera, F.: A study of the behaviour of linguistic fuzzy rule based classification systems in the framework of imbalanced data-sets. Fuzzy Sets Syst. 159(18), 2378-2398 (2008).
48. García, S., Fernández, A., Luengo, J., Herrera, F.: A study of statistical techniques and performance measures for genetics-based machine learning: accuracy and interpretability. Soft Comput. (2009). doi: 10. 1007/s00500-008-0392-y.
49. Orriols-Puig, A., Bernadó-Mansilla, E.: Evolutionary rule-based systems for imbalanced datasets. Soft Comput. 13(3), 213-225 (2009).
50. García, S., Molina, D., Lozano, M., Herrera, F.: A study on the use of non-parametric tests for analyzing the evolutionary algorithms' behaviour: a case study on the CEC'2005 special session on real parameter optimization. J Heurist. 15, 617-644 (2009). doi: 10. 1007/s10732-008-9080-4.
51. Luengo, J., García, S., Herrera, F.: A study on the use of statistical tests for experimentation with neural networks: analysis of parametric test conditions and non-parametric tests. Expert Syst. Appl. 36(2009), 7798-7808 (2009).