Classifying imbalanced data using a Bagging Ensemble Variation (BEV)

Proceedings of the Annual Southeast Conference

Volumn 2007, Issue , 2007, Pages 203-208

  Cen, Li ^a  

^a MIDDLE TENNESSEE STATE UNIVERSITY   (United States)

Author keywords

Classification; Decision tree; Imbalanced data; Machine learning; Support vector machine

Indexed keywords

BAGGING ENSEMBLE VARIATION (BEV); CLASSIFICATION ENSEMBLE; IMBALANCED DATA; MINORITY CLASS DATA;

CLASSIFICATION (OF INFORMATION); DATA ACQUISITION; DECISION TREES; INFORMATION SYSTEMS; INFORMATION USE; SUPPORT VECTOR MACHINES;

DATA REDUCTION;

EID: 34248390180     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1233341.1233378     Document Type: Conference Paper

References (29)

1. Palmer, C. R. and Faloutsos, C., Density biased sampling: an improved method for data mining and clustering, CMU-CS-99-113, 1999.
2. Weiss, G.M. and Provost, F., The effects of classification distribution on classifier learning: an empirical study, Technical Report ML-TR-44, Department of Computer Science, Rutgers University, Aug. 2001.
3. Stone, D.H. and Moyar G.J., Wheel shelling and spalling - an interpretive review, Rail Transportation 1989, ASME, pp.19-31, 1989.
4. Marais, J.J, Wheel failures on heavy haul freight wheels due to subsurface effects, Proc 12th International Wheelset Congress, Qingdao, China, pp. 306-314, 1998.
5. Mutton, P.J., Epp, C.J. and Dudek, J., Rolling contact fatigue in railway wheels under high axle loads, Wear. Vol. 144, pp. 139-152, 1991.
6. Ekberg, A., E. Kabo, and H. Andersson., An engineering model for prediction of rolling contact fatigue of railway wheels, Fatigue & Fracture of Engineering Materials and Structures. Vol. 25, pp. 899-909, 2002.
7. th European Conference on Machine Learning, 1998.
8. Burges, C. J. C. A tutorial on support vector machines for pattern recognition, Knowledge discovery and Data Mining, 2, pp. 1-43, 1998.
9. Weston, J. and Watkins, C. Multi-class support vector machines. Royal Holloway, University of London. Technical Report, CSD-TR-98-04.
10. Joachims, T. Making large scale SVM learning practical. Advances in Kernel Methods - Support Vector Learning, ed. Scholkopf, B, Burges, C. and Smola, A. MIT Press, Cambridge, USA, 1998.
11. Ding, C. H.Q. and Dubchak, I., Multi-class protein fold recognition using support vector machines and neural networks.
12. Quinlan, J. R., C4.5: Programs for machine learning, Morgan Kaufmann Publishers, Inc. 1993.
13. Caruana, R. and Niculescu-Mizil, A. An empirical comparision of supervised learning algorithms. Proceedings of the International Conference on Machine Learning, 2006.
14. Mitchell, T. M., Machine Learning. McGraw-Hill Companies, Inc., 1997.
15. Freund, Y. and Schapire, R. E., Experiments with a new boosting algorithm, Proceedings of the Thirteenth International Conference on Machine Learning, pp.148-156, 1996.
16. Breiman, L., Bagging predictors, Machine Learning, 24, pp. 123-140, 1996.
17. Friedman, J. H. and Popescu, B. E., Predictive learning via rule ensembles. Technical Report, Stanford University, 2005.
18. Cesa-Bianchi, N. Gentile, C. and Zaniboni, L. Hierarchical classification: combining Bayes with SVM. Proceedings of the International Conference on Machine Learning, 2006.
19. th International Conference on machine Learning. 1997.
20. Japkowicz, N., The class imbalance problem: significance and strategies. Proceedings of the 2000 International Conference on Artificial intelligence: Special Track on Inductive Learning, Las Vegas, Nevada.
21. Chawla, N.V., Bowyer, K.W., Hall, L.O., and Kegelmeyer, W.P., SMOTE: synthetic minority over-sampling technique, Journal of Artificial Intelligence Research. Vol (16), pp. 341-378, 2002.
22. Akbani, R., Kwek, S., and Japkowicz, N. Applying support vector machines to imbalanced datasets, Proceedings of the 15th European Conference on Machine Learning, pp. 39-50, Pisa, Italy.
23. Phua, C., Alahakoon, D., and Lee, V., Minority report in fraud detection: classification of skewed data, SigKdd Explorations. Vol 6(1), pp. 50-59.
24. Cover, T. and Hart P., "Nearest neighbor pattern classification". IEEE Transactions on Information Theory, Vol (13), pp 21-27, 1967.
25. Dasarathy, B. V., "Nearest-neighbor classification techniques". IEEE Computer Society Press, Los Alomitos, CA.
26. Globig, C. and Wess, S., Symbolic learning and nearest-neighbor classification. 1994.
27. Anand, R., Mehrotra, K. G., Nohan, C. K., and Ranka, S., "An improved algorithm for neural network classification of imbalanced training set". IEEE Transactions on Neural Networks, Vol. 4, No. 6, pp 962-969, November 1993.
28. McCallum, A. and Nigam, K., "A comparison of event models for naïve bayes text classification". Proceedings of the AAAI-98 workshop on Learning for text categorization, 1998.
29. Domingos, P. and Pazzani, M., "Beyond independence: conditions for the optimality of the simple Bayesian classifier. Proceedings of the Thirteenth International Conference on Machine Learning (ICML), 1996.