Research of Fuzzy v-Relative Margin Machine Based on Total Margin

The between-class margin and within-class cohesion are two important factors impacting on the performance of classifiers.This paper presents a novel classifier called total margin based fuzzy v-relative margin machine(TMF-vRMM) based on the idea of fuzzy support vector machine(FSVM) and total margin.Although it can be seen as a modified class of the classic RMM,TMF-vRMM has better theoretical classification performance than RMM.The proposed method solves not only the over-fitting problem resulted from outliers with the approaches of fuzzification of the penalty and total margin algorithm,but also the imbalanced datasets by using different cost algorithm,thus obtaining a lower generalization error bound.Experimental results obtained with synthetic and real datasets respectively show that the algorithm proposed in the paper is stable and superior to other related diagrams.