A novel feature selection method for twin support vector machine

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Both support vector machine (SVM) and twin support vector machine (TWSVM) are powerful classification tools. However, in contrast to many SVM-based feature selection methods, TWSVM has not any corresponding one due to its different mechanism up to now. In this paper, we propose a feature selection method based on TWSVM, called FTSVM. It is interesting because of the advantages of TWSVM in many cases. Our FTSVM is quite different from the SVM-based feature selection methods. In fact, linear SVM constructs a single separating hyperplane which corresponds a single weight for each feature, whereas linear TWSVM constructs two fitting hyperplanes which corresponds to two weights for each feature. In our linear FTSVM, in order to link these two fitting hyperplanes, a feature selection matrix is introduced. Thus, the feature selection becomes to find an optimal matrix, leading to solve a multi-objective mixed-integer programming problem by a greedy algorithm. In addition, the linear FTSVM has been extended to the nonlinear case. Furthermore, a feature ranking strategy based on FTSVM is also suggested. The experimental results on several public available benchmark datasets indicate that our FTSVM not only gives nice feature selection on both linear and nonlinear cases but also improves the performance of TWSVM efficiently.