Fuzzy Support Vector Machines Based on λ —Cut

摘要

A new Fuzzy Support Vector Machines (λ —FSVMs) based on λ —cut is proposed in this paper. The proposed learning machines combine the membership of fuzzy set with support vector machines. The λ —cut set is introduced to distinguish the training samples set in term of the importance of the data. The more important sets are selected as new training sets to construct the fuzzy support vector machines. The benchmark two-class problems and multi-class problems datasets are used to test the effectiveness and validness of λ — FSVMs. The experiment results indicate that λ —FSVMs not only has higher precision but also solves the overfitting problem of the support vector machines more effectively.