Improved Robust Fuzzy Twin Support Vector Machine Algorithm

Since the fuzzy twin support vector machine（FTSVM）algorithm is still sensitive to noise and prone to over fitting, as well as cannot effectively distinguish support vectors from outliers. This paper proposes an improved robust fuzzy twin support vector machine（IRFTSVM）. Firstly, a new kind of mixed membership function is constructed by combining the intra- class hyperplanemembership function and the improved k - nearest neighbor membership function. Secondly, a regularization term and the additional constraint are brought into the objective function to minimize the structural risk, avoid the computationof inverse matrix, and nonlinear problems can be directed from linear case as the classical SVM algorithm. Finally, the hinge loss function is replaced by the pinball loss function to reduce the noise sensitivity. In addition, the proposed algorithm is assessed and compared with SVM, TWSVM, FTSVM, PTSVM and TBSVM on some UCI datasets and an artificial dataset. The experimental results show that the proposed algorithm is satisfactory. © 2016 Chinese Medical Journals Publishing House Co.Ltd. All rights reserved.