模糊规则演化的框架

Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753) Pub Date : 2004-06-19 DOI:10.1109/CEC.2004.1331104

Jonatan Gómez

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引用次数: 1

摘要

本文提出了一种基于遗传模糊规则的分类器框架。首先，根据模糊类二值化方案，将一个分类问题划分为若干两类问题;其次，使用密歇根迭代学习方法对每个两类问题演化出模糊规则;最后，利用模糊类二值化方案对演化出的模糊规则进行整合。特别地，根据所提出的框架实现了一些编码方案，并比较了它们的性能。实验是用不同的公共数据集进行的。

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A framework for evolving fuzzy rule

This work presents a framework for genetic fuzzy rule based classifier. First, a classification problem is divided into several two-class problems following a fuzzy class binarization scheme; next, a fuzzy rule is evolved for each two-class problem using a Michigan iterative learning approach; finally, the evolved fuzzy rules are integrated using the fuzzy class binarization scheme. In particular, some encoding schemes are implemented following the proposed framework and their performance is compared. Experiments are conducted with different public available data sets.

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Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753)

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