A Projection and Contraction Method for Nonparallel Hyperplane Circular Cone Programming Support Vector Machine

A second-order cone programming support vector machine (SOCP-SVM) method is proposed to realize the binary classification faster and better. This method combines the advantages of better classification performance of nonparallel hyperplane second-order cone programming support vector machine (SOCP-NHSVM), and represents SOCP-NHSVM as a convex quadratic nonparallel hyperplane circular cone programming support vector machine (CQCCP-NHSVM), and finally uses the projection and contraction method to solve it. Experiments on the benchmark dataset of the UCI repository show that the proposed method can improve the computational efficiency and obtain the accuracy value, F-measure value and G-mean value, which are similar to the SOCP-NHSVM of the linear classifier. In the case of kernel-based nonlinear classification, the proposed method can achieve similar performance under three model evaluation indexes, and greatly shorten the running time of the program.