An Improved Fuzzy Support Vector Machine for Credit Rating

In order to classify data with noises or outliers, Fuzzy support vector machine (FSVM) improve the generalization power of traditional SVM by assigning a fuzzy membership to each input data point. In this paper, an improved FSVM based on vague sets is proposed by assigning a truth-membership and a false-membership to each data point. And we reformulate the improved FSVM so that different input points can make different contributions to decision hyperplane. The effectiveness of the improved FSVM is verified in credit rating; the experiment results show that our method is promising.

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