A New Approach to Attribute Importance Ranking for Constructing Classification Rules Based on SVR

How to extract rules from trained SVMs has become an important preprocessing technique for data mining, pattern classification, and so on. There are two key problems required to be solved in the SVM based classification rule extraction, i.e. the attribute selection and the discretization to continuous attributes. In this paper, the differential characteristic of SVR (Support vector regression) is discussed. A new measure for determining the importance level of the attributes based on the trained SVR classifiers is proposed. Based on this new measure, a new approach for rule extraction from trained SVR classifiers is proposed. A new algorithm for rule extraction is given. The performance of the new approach is demonstrated by several computing cases. Experiment results show that the proposed approach can improve the validity of the extracted rules remarkably compared to other rule extracting approaches, especially for complicated classification problems.