Proximal Support Vector Machines for Samples with Unbalanced Classification
TAO XiaoYan1,2, JI HongBing1, Dong ShuFu2
1.School of Electronic Engineering, Xidian University, Xi’an 710071 2.Institute of Telecommunication Engineering, Air Force Engineering University, Xi’an 710077
Abstract：Aiming at the problem that unbalanced data classification is disregarded in the standard Proximal Support Vector Machines (PSVM), a modified PSVM algorithm is presented, namely MPSVM. The different penalty factors are assigned to the positive and negative training sets according to the unbalanced population. The penalty values are transformed into a diagonal matrix. Then the decision functions for the linear and nonlinear MPSVM are achieved. Finally, the comparisons of algorithmic principle and performance are drawn. The experimental results show that MPSVM has a better generalization performance than PSVM and higher efficiency than the unbalanced SVM.
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