JACIII Vol.11 No.4 pp. 389-395
doi: 10.20965/jaciii.2007.p0389


Classification Rule Extraction Based on Relevant, Irredundant Attributes and Rule Enlargement

George Lashkia*, Laurence Anthony**, and Hiroyasu Koshimizu*

*School of Information Science and Technology, Chukyo University, 101 Tokodate, Kaizu-cho, Toyota 470-0393, Japan

**School of Science and Engineering, Waseda University, 3-4-1 Okubo, Shinjuku-ku, Tokyo 169-8555, Japan

April 18, 2006
August 11, 2006
April 20, 2007
classification rules, prime test, relevant attributes, rule merging, inductive learning

In this paper we focus on the induction of classification rules from examples. Conventional algorithms fail in discovering effective knowledge when the database contains irrelevant information. We present a new rule extraction method, RGT, which tackles this problem by employing only relevant and irredundant attributes. Simplicity of rules is also our major concern. In order to create simple rules, we estimate the purity of patterns and propose a rule enlargement approach, which consists of rule merging and rule expanding procedures. In this paper, we describe the methodology for the RGT algorithm, discuss its properties, and compare it with conventional methods.

Cite this article as:
George Lashkia, Laurence Anthony, and Hiroyasu Koshimizu, “Classification Rule Extraction Based on Relevant, Irredundant Attributes and Rule Enlargement,” J. Adv. Comput. Intell. Intell. Inform., Vol.11, No.4, pp. 389-395, 2007.
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