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CLC number: TP311

On-line Access: 2011-05-09

Received: 2010-06-18

Revision Accepted: 2010-10-08

Crosschecked: 2011-03-31

Cited: 1

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Journal of Zhejiang University SCIENCE C 2011 Vol.12 No.5 P.379-386


Extracting classification rules based on a cumulative probability distribution approach

Author(s):  Jr-shian Chen

Affiliation(s):  Department of Computer Science and Information Management, Hungkuang University, Taiwan 433, Taichung

Corresponding email(s):   jschen@sunrise.hk.edu.tw

Key Words:  Cumulative probability distribution approach (CPDA), Classification rule, C4.5

Jr-shian Chen. Extracting classification rules based on a cumulative probability distribution approach[J]. Journal of Zhejiang University Science C, 2011, 12(5): 379-386.

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DOI - 10.1631/jzus.C1000205

This paper deals with a reinforced cumulative probability distribution approach (CPDA) based method for extracting classification rules. The method includes two phases: (1) automatic generation of the membership function, and (2) use of the corresponding linguistic data to extract classification rules. The proposed method can determine suitable interval boundaries for any given dataset based on its own characteristics, and generate the fuzzy membership functions automatically. Experimental results show that the proposed method surpasses traditional methods in accuracy.

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