Structural improved regular simplex support vector machine for multiclass classification

Abstract

Although the structural regularized support vector machine (SRSVM) can enhance the generalization capability of the standard support vector machine (SVM), its current version is used only for binary classification. To make SRSVM adapt to the K-class classification, the most direct approach is combining it with partitioning strategies, which may however lead to the following shortcomings: (1) Extracting structural information repeatedly for individual classifiers based on different class partitions increases the computational complexity. (2) Individual classifiers can hardly utilize complete data structural information. Under the basic framework of regular simplex support vector machine (RSSVM), we developed a novel structural improved regular simplex support vector machine (SIRSSVM). SIRSSVM generates only a single primal optimization problem, into which the data structural information within all classes is embedded, rather than using only partial structural information to construct individual classifiers as partitioning strategies do. Additionally, we modified the sequential minimization optimization (SMO)-type solver for RSSVM to adapt the proposed SIRSSVM model. Experimental results verified that our SIRSSVM could achieve excellent performance on both generalization capability and training efficiency.