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Extracting classification rules based on a cumulative probability distribution approach

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Abstract

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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  1. Department of Computer Science and Information Management, Hungkuang University, Taiwan, 433, Taichung

    Jr-shian Chen

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  1. Jr-shian Chen
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Chen, Js. Extracting classification rules based on a cumulative probability distribution approach. J. Zhejiang Univ. - Sci. C 12, 379–386 (2011). https://doi.org/10.1631/jzus.C1000205

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  • DOI: https://doi.org/10.1631/jzus.C1000205

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