r-SVMT: Discovering the knowledge of association rule over SVM classification trees

Abstract

This paper presents a novel method of rule extraction by encoding the knowledge of the data into an SVM classification tree (SVMT), and decoding the trained SVMT into a set of linguistic association rules. The method of rule extraction over the SVMT (r-SVMT), in the spirit of decision-tree rule extraction, achieves rule extraction not only from SVM, but also over the obtained decision-tree structure. The benefits of r-SVMT are that the decision-tree rule provides better comprehensibility, and the support-vector rule retains the good classification accuracy of SVM. Furthermore, the r-SVMT is capable of performing a very robust classification on such datasets that have seriously, even overwhelmingly, class-imbalanced data distribution, which profits from the super generalization ability of SVMT owing to the aggregation of a group of SVMs. Experiments with a gaussian synthetic data, seven benchmark cancers diagnosis have highlighted the utility of SVMT and r-SVMT on encoding and decoding rule knowledge, as well as the superior properties of r-SVMT as compared to a completely support-vector based rule extraction.