关键词: 支持向量机, 非均衡数据, 分类, 隶属度函数

Abstract: Since SVM is sensitive to noises or outliers in the training set and the classification of unbalance data is unfair to the rare class，a new fuzzy Support Vector Machine is presented with theoretical analysis given.By properly designing a new fuzzy membership function，the proposed algorithm can compensate the ill-effect of tendency and also can strengthen the ability to detect noises thus improves the accuracy.Simulations on unbalenced data with noise show that，compared with traditional SVM and FSVM，this algorithm has better classification ability.

Key words: Support Vector Machine, unbalanced data, classification, membership function