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

Content Provider	IEEE Xplore Digital Library
Author	Dexian Zhang Zhixiao Yang Yanfeng Fan Ziqiang Wang
Copyright Year	2008
Description	Author affiliation: Coll. of Inf. Sci. & Eng., Henan Univ. of Technol., Zhengzhou (Dexian Zhang; Zhixiao Yang; Ziqiang Wang) \|\| Comput. Coll., Northwestern Polytech. Univ., Xi'an (Yanfeng Fan)
Abstract	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.
Starting Page	75
Ending Page	80
File Size	282538
Page Count	6
File Format	PDF
ISBN	9780769533056
DOI	10.1109/FSKD.2008.306
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-10-18
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Knowledge engineering attribute importance ranking Educational institutions Data engineering Data mining SVR Support vector machines Information science Neural networks Support vector machine classification rule extraction Mutual information Fuzzy systems
Content Type	Text
Resource Type	Article

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