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Imbalanced data classification based on scaling kernel-based support vector machine

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Abstract

In many classification problems, the class distribution is imbalanced. Learning from the imbalance data is a remarkable challenge in the knowledge discovery and data mining field. In this paper, we propose a scaling kernel-based support vector machine (SVM) approach to deal with the multi-class imbalanced data classification problem. We first use standard SVM algorithm to gain an approximate hyperplane. Then, we present a scaling kernel function and calculate its parameters using the chi-square test and weighting factors. Experimental results on KEEL data sets show the proposed algorithm can resolve the classifier performance degradation problem due to data skewed distribution and has a good generalization.

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Acknowledgments

This work is partly supported by National Natural Science Foundation of China (No. 61373127), the China Postdoctoral Science Foundation (No. 20110491530), and the University Scientific Research Project of Liaoning Education Department of China (No. 2011186).

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Authors and Affiliations

  1. School of Computer and Information Technology, Liaoning Normal University, No.1, Liushu South Street, Ganjingzi District, Dalian, 116081, Liaoning Province, China

    Yong Zhang, Panpan Fu & Wenzhe Liu

  2. College of Information Engineering, Suzhou University, Suzhou, 234000, China

    Panpan Fu & Guolong Chen

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  1. Yong Zhang
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  2. Panpan Fu
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  3. Wenzhe Liu
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  4. Guolong Chen
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Correspondence to Yong Zhang.

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Zhang, Y., Fu, P., Liu, W. et al. Imbalanced data classification based on scaling kernel-based support vector machine. Neural Comput & Applic 25, 927–935 (2014). https://doi.org/10.1007/s00521-014-1584-2

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  • DOI: https://doi.org/10.1007/s00521-014-1584-2

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