New method for support vector machine based on imbalanced data
非均衡数据的支持向量机新方法*

Since support vector machine is unfair to the rare class for the classification of imbalanced data, proposed an adjustment method of the separating hyperplane. Based on Fisher discrimination, got the projected class mean and variance are by projecting two classes samples onto the normal vector of the separating hyperplane, then adjusted the threshold of the hyperplane, according to the principle that error probability of two classes are equal. The proposed algorithm could compensate the ill-effect of tendency and improved the accuracy. Simulations on imbalanced artificial and real data show that the feasibility and validity of the proposed method.