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Membership evaluation and feature selection for fuzzy support vector machine based on fuzzy rough sets

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Abstract

A fuzzy support vector machine (FSVM) is an improvement in SVMs for dealing with data sets with outliers. In FSVM, a key step is to compute the membership for every training sample. Existing approaches of computing the membership of a sample are motivated by the existence of outliers in data sets and do not take account of the inconsistency between conditional attributes and decision classes. However, this kind of inconsistency can affect membership for every sample and has been considered in fuzzy rough set theory. In this paper, we develop a new method to compute membership for FSVMs by using a Gaussian kernel-based fuzzy rough set. Furthermore, we employ a technique of attribute reduction using Gaussian kernel-based fuzzy rough sets to perform feature selection for FSVMs. Based on these discussions we combine the FSVMs and fuzzy rough sets methods together. The experimental results show that the proposed approaches are feasible and effective.

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Acknowledgments

This paper is supported by NSFCs (70871036, 60773062, 60903088, 60903089), by the natural science foundation of Hebei Province (F2008000635, F2009000231, F2009000227) and by the Scientific Research Project of Department of Education of Hebei Province (2009410).

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Authors and Affiliations

  1. Department of Mathematics, Harbin Institute of Technology, Harbin, 150001, People’s Republic of China

    Qiang He & Congxin Wu

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  1. Qiang He
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  2. Congxin Wu
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Correspondence to Qiang He.

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He, Q., Wu, C. Membership evaluation and feature selection for fuzzy support vector machine based on fuzzy rough sets. Soft Comput 15, 1105–1114 (2011). https://doi.org/10.1007/s00500-010-0577-z

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