A New Approach to Symbolic Classification Rule Extraction Based on SVM
There still exist two key problems required to be solved in the classification rule extraction, i.e. how to select attributes and discretize continuous attributes effectively. The lack of efficient heuristic information is the fundamental reason that affects the performance of currently used approaches. In this paper, a new measure for determining the importance level of the attributes based on the trained SVM is proposed, which is suitable for both continuous attributes and discrete attributes. Based on this new measure, a new approach for rule extraction from trained SVM and classification problems with continuous attributes is proposed. The performance of the new approach is demonstrated by several computing cases. The experimental results prove that the approach proposed can improve the validity of the extracted rules remarkably compared with other rule extracting approaches, especially for the complicated classification problems.
KeywordsMutual Information Class Label Continuous Attribute Attribute Selection Rule Extraction
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