¹College of Computer Science and Technology, Jilin University, Changchun, China
²College of Computer Science and Information Technology, Northeast Normal University, Changchun, China
³College of computer, Jilin Normal University,Siping , China

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Abstract—Propose a novel approach of rule extracting based on attribute significance and decision classification (REBSC). Condition attributes are only discretized with their own features in local discretization theory, and the eventual rule set normally can be achieved after attribute reduction. The REBSC approach given in this paper makes the most of each condition attribute significance and classification object, generates minimum decision rules without attribute reduction. Experiment one fully demonstrates the reasonability of the REBSC algorithm. Experiment two further proves its validity and testifies the significance of breakpoint division.

Index Terms—Rough set, attribute significance, discretization, decision rule, attribute reduction.

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Cite: Yandong Zhai, Chunguang Zhou, Yingjuan Sun, Dongbing Pu, YingHui Sun, "Approach of Rule Extracting Based on Attribute Significance and Decision Classification," Journal of Computers vol. 7, no. 2, pp. 514-520, 2012.