To deal with the consistency problem of training process and decision process in Generalized Eigenvalue Proximal Support Vector Machine (GEPSVM), an improved version of eigenvalue proximal support vector machine, called IGEPSVM for short is proposed. At first, IGEPSVM for binary classification problem is proposed, and then Multi-IGEPSVM is also presented for multi-class classification problem based on “one-versus-rest” strategy. The main contributions of this paper are as follows. The generalized eigenvalue decomposition problems are replaced by the standard eigenvalue decomposition problems, leading to simpler optimization problems. An extra parameter is introduced, which can adjust the performance of the model and improve the classification accuracy of GEPSVM. A corresponding multi-class classification algorithm is proposed, which is not studied in GEPSVM. Experimental results on several datasets illustrate that IGEPSVM is superior to GEPSVM in both classification accuracy and training speed.
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