关键词: 离群点, 不平衡数据, 密度峰（DP）, 加权模糊支持向量机, 模糊隶属度, 可信性

Abstract: Considering that SVM（Support Vector Machine） has relatively low classification performance in the case of outliers and unbalanced data, a weighted fuzzy support vector machine was proposed. And the fuzzy membership in that paper is not a good measure for the contribution of the sample to the determination of the optimal separating hyperplane. Thus, a DP（Density Peaks）clustering, creditability weighted fuzzy support vector machine is proposed. Outliers are found by DP clustering, then the outliers are eliminated. The distance from every sample to the hyperplane determined by DEC（Different Error Costs）is used to bulid the initial degree of membership. Then the degree of membership is updated with the improved FSVM-CIL（Fuzzy Support Vector Machines for Class Imbalance Learning）. Finally, some samples are removed, which reduces the number of samples and reduces the impact of data imbalances. The effectiveness of the proposed algorithm is verified by experiments.

Key words: outliers, unbalanced data, Density Peaks（DP）, weighted fuzzy support vector machine, fuzzy membership;creditability