A novel fuzzy compensation multi-class support vector machine

This paper presents a novel fuzzy compensation multi-class support vector machine (FCM-SVM) to improve the outlier and noise sensitivity problem of traditional support vector machine (SVM) for multi-class data classification. The basic idea is to give the dual effects to penalty term through treating every data point as both positive and negative classes, but with different memberships. We fuzzify penalty term, compensate weight to classification, reconstruct the optimization problem and its restrictions, reconstruct Lagrangian formula, and present the theoretic deduction. By this way the new fuzzy compensation multi-class support vector machine is expected to have more generalization ability while preserving the merit of insensitive to outliers. Experimental results on benchmark data set and real data set show that the proposed method reduces the effect of noise data and yields higher classification rate than traditional multi-class SVM does.