[KSCI] Korea Science Citation Index Service

http://dx.doi.org/10.5392/IJoC.2012.8.2.007

Improving the Error Back-Propagation Algorithm for Imbalanced Data Sets

Oh, Sang-Hoon (Department of Information Communication Engineering Mokwon University)

Publication Information

International Journal of Contents / v.8, no.2, 2012 , pp. 7-12 More about this Journal

Abstract

Imbalanced data sets are difficult to be classified since most classifiers are developed based on the assumption that class distributions are well-balanced. In order to improve the error back-propagation algorithm for the classification of imbalanced data sets, a new error function is proposed. The error function controls weight-updating with regards to the classes in which the training samples are. This has the effect that samples in the minority class have a greater chance to be classified but samples in the majority class have a less chance to be classified. The proposed method is compared with the two-phase, threshold-moving, and target node methods through simulations in a mammography data set and the proposed method attains the best results.

Keywords

Imbalanced Data; Error Back-Propagation; Error Function; Mammography;

Citations & Related Records

Reference

1	P. Kang and S. Cho, "EUS SVMs: ensemble of under-sampled SVMs for data imbalance problem, " Proc. ICONIP'06, 2006, p. 837-846.
2	L. Bruzzone and S. B. Serpico, "Classification of Remote-Sensing Data by Neural Networks," Pattern Recognition Letters, vol.18, 1997, pp. 1323-1328. DOI ScienceOn
3	Z.-H. Zhou and X.-Y. Liu, "Training Cost-Sensitive Neural Networks with Methods Addressing the Class Imbalance Problem," IEEE Trans. Know. and Data Eng., vol.18, no. 1, Jan. 2006, pp. 63-77. DOI ScienceOn
4	S.-H. Oh, "Improving the Error Back-Propagation Algorithm with a Modified Error Function," IEEE Trans. Neural Networks, vol.8, 1997, pp. 799-803. DOI ScienceOn
5	S.-H. Oh, "Error Back-Propagation Algorithm for Classification of Imbalanced Data," Neurocomputing, vol.74, 2011, pp. 1058-1061. DOI ScienceOn
6	H. White, "Learning in Artificial Neural Networks: A Statistical Perspective," Neural Computation, vol.1, no.4, Winter 1989, pp. 425-464. DOI
7	S.-H. Oh, "A Statistical Perspective of Neural Networks for Imbalanced Data Problems," Int. Journal of Contents, vol.7,2011,pp.1-5.
8	A. van Ooyen and B. Nienhuis, "Improving the convergence of the backpropagation algorithm," Neural Networks, vol.5, 1992, pp. 465-471. DOI ScienceOn
9	Y.-M. Huang, C.-M. Hung, and H. C. Jiau, "Evaluation of Neural Networks and Data Mining Methods on a Credit Assessment Task for Class Imbalance Problem," Nonlinear Analysis, vol.7, 2006, pp. 720-747. DOI ScienceOn
10	H. Zhao, "Instance Weighting versus Threshold Adjusting for Cost-Sensitive Classification," Knowledge and Information Systems, vol.15, 2008, pp. 321-334. DOI
11	R. Bi, Y. Zhou, F. Lu, and W. Wang, "Predicting gene ontology functions based on support vector machines and statistical significance estimation," Neurocomputing, vol.70, 2007, pp.718-725. DOI ScienceOn
12	N. V. Chawla, K. W. Bowyer, L. O. all, and W. P. Kegelmeyer, "SMOTE: Synthetic Minority Over-sampling Technique," J. Artificial Intelligence Research, vol.16, 2002, pp. 321-357.
13	F. Provost and T. Fawcett, "Robust Classification for Imprecise Environments," Machine Learning, vol.42, 2001, pp. 203-231. DOI
14	D. E. Rumelhart and J. L. McClelland, Parallel Distributed Processing, Cambridge, MA, 1986.