计算机工程与应用2019，Vol.55Issue(10)：169-178,10.DOI:10.3778/j.issn.1002-8331.1804-0054
DP聚类的可信性加权模糊支持向量机
DP Clustering, Creditability Weighted Fuzzy Support Vector Machine
摘要
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 pro-posed algorithm is verified by experiments.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Key words
outliers/unbalanced data/Density Peaks(DP)/weighted fuzzy support vector machine/fuzzy membership/creditability分类
信息技术与安全科学引用本文复制引用
盛晓遐,杨志民,王甜甜..DP聚类的可信性加权模糊支持向量机[J].计算机工程与应用,2019,55(10):169-178,10.基金项目
国家自然科学基金(No.61771223). （No.61771223）
