Abstract
This paper presents a fuzzy support vector classifier by integrating modified fuzzy c-means clustering based on Mahalanobis distance into fuzzy support vector data description. The proposed algorithm can be used to deal with the outlier sensitivity problem in traditional multi-class classification problems. The modified fuzzy c-means clustering algorithm based on Mahalanobis distance takes into the samples’ correlation account, and is improved to generate different weight values for main training data points and outliers according to their relative importance in the training data. Experimental results show that the proposed method can reduce the effect of outliers and give high classification accuracy.
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Acknowledgements
This work is supported by the Liaoning Doctoral Research Foundation of China (Grant No. 20081079), and the University Scientific Research Project of Liaoning Education Department of China (Grant No. 2008347). The authors are grateful for the anonymous reviewers who made constructive comments.
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Zhang, Y., Xie, F., Huang, D. et al. Support vector classifier based on fuzzy c-means and Mahalanobis distance. J Intell Inf Syst 35, 333–345 (2010). https://doi.org/10.1007/s10844-009-0102-y
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DOI: https://doi.org/10.1007/s10844-009-0102-y