Attribute Importance Ranking Based on Support Vectors for Classification Rule Extraction
Attribute importance ranking still is a key problem required to be solved for the classification rule extraction. 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 support vectors of trained SVMs 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 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.
attribute importance ranking classification ruleextraction SVM
Dexian Zhang Sumin Jiao Yanfeng Fan Zhixiao Yang
college of Information Science and Engineering,Henan University of Technology,Zhengzhou 450052,China College of Computer Science,Northwestern Polytecnical University,Xian 710072,China
国际会议
2008高等智能国际会议(2008 International Conference on Advanced Intelligence)
北京
英文
2008-10-18（万方平台首次上网日期，不代表论文的发表时间）