Constructing Classification Rules Based on SVR and Its Derivative Characteristics

Authors:
Dexian Zhang;Zhixiao Yang;Yanfeng Fan;Ziqiang Wang
Affiliations:
College of Information Science and Engineering, Henan University of Technology, Zhengzhou 450052, China;College of Information Science and Engineering, Henan University of Technology, Zhengzhou 450052, China;Computer College, Northwestern Polytecnical University, Xi'an 710072, China;College of Information Science and Engineering, Henan University of Technology, Zhengzhou 450052, China
Venue:
ADMA '07 Proceedings of the 3rd international conference on Advanced Data Mining and Applications
Year:
2007

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Abstract

Support vector regression (SVR) is a new technique for pattern classification , function approximation and so on. In this paper we propose an new constructing approach of classification rules based on support vector regression and its derivative characteristics for the classification task of data mining. a new measure for determining the importance level of the attributes based on the trained SVR is proposed. Based on this new measure, a new approach for clas-sification rule construction using trained SVR is proposed. The performance of the new approach is demonstrated by several computing cases. The experimen-tal results prove that the approach proposed can improve the validity of the ex-tracted classification rules remarkably compared with other constructing rule approaches, especially for the complicated classification problems.