Simultaneous feature selection and classification via Minimax Probability Machine

This paper presents a novel method for simultaneous feature selection and classification by incorporating a robust L-1-norm into the objective function of Minimax Probability Machine (MPM). A fractional programming framework is derived by using a bound on the misclassification error involving the mean and covariance of the data. Furthermore, the problems are solved by the Quadratic Interpolation method. Experiments show that our methods can select fewer features to improve the generalization compared to MPM, which illustrates the effectiveness of the proposed algorithms.