Rule extraction from trained support vector machines

Abstract

Support vector machine (SVM) is applied to many research fields because of its good generalization ability and solid theoretical foundation. However, as the model generated by SVM is like a black box, it is difficult for user to interpret and understand how the model makes its decision. In this paper, a hyperrectangle rules extraction (HRE) algorithm is proposed to extract rules from trained SVM. Support vector clustering (SVC) algorithm is used to find the prototypes of each class, then hyperrectangles are constructed according to the prototypes and the support vectors (SVs) under some heuristic conditions. When the hyperrectangles are projected onto coordinate axes, the ifthen rules are obtained. Experimental results indicate that HRE algorithm can extract rules efficiently from trained SVM and the number and support of obtained rules can be easily controlled according to a user-defined minimal support threshold. © Springer-Verlag Berlin Heidelberg 2005.