A new approach to division of attribute space for SVR based classification rule extraction

Abstract

SVM based rule extraction 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 classification rule extraction based on SVMs, i.e. the attribute importance ranking and the discretization to continuous attributes. In the paper, firstly, a new measure for determining the importance level of the attributes based on the trained SVR (Support vector re-gression) classifiers is proposed. Based on this new measure, a new approach for the division to continuous attribute space based on support vectors is pre-sented. A new approach for classification rule extraction from trained SVR classifiers is given. The performance of the new approach is demonstrated by several computing cases. The experimental results prove that the proposed ap-proach proposed can improve the validity of the extracted classification rules remarkably compared with other constructing rule approaches, especially for complicated classification problems. © 2008 Springer-Verlag Berlin Heidelberg.