Research of classification hypersurface based on tangent circular arc smooth SVM (TAC-SSVM)

The lack of efficient heuristic information is the fundamental reason that causes the low effectiveness of currently used approaches for extracting symbolic rules. Classification hypersurface are direct heuristic information for selecting attributes. In SVM (Support Vector Machine), classification hypersurface is emphasized because of the direct induction of the support vectors. In this paper, a new SVM model and a new measurement method of the classification power of attributes on the basis of the characteristics of the classification hypersurfaces based on the proposed SVM model is presented. The experimental results prove that the approach proposed can improve the validity of the extracted rules remarkably compared with other rule extracting approach.