子群发现模糊规则提取的多目标遗传算法

2007 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making Pub Date : 2007-04-01 DOI:10.1109/MCDM.2007.369416

M. J. Jesús, P. González, F. Herrera

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引用次数: 40

摘要

提出了一种求解子群发现模糊规则的多目标遗传算法。这种模糊规则使我们能够以一种可解释和可理解的形式表示关于感兴趣的模式的知识，这种形式可以被专家使用。本文提出的多目标算法定义了三个目标。其中一个被用作对规则的限制，以获得由一组完全不同的规则组成的帕累托前沿，这些规则在示例上具有高度的覆盖率。另外两个目标考虑到规则的支持和信任。使用上述目标作为限制，我们可以提取一组规则，这些规则描述了大多数示例中更完整的信息。将该算法应用于市场问题的实验评估表明，该算法为专家提供了新颖而有价值的知识

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Multiobjective Genetic Algorithm for Extracting Subgroup Discovery Fuzzy Rules

This paper presents a multiobjective genetic algorithm for obtaining fuzzy rules for subgroup discovery. This kind of fuzzy rules lets us represent knowledge about patterns of interest in an explanatory and understandable form which can be used by the expert. The multiobjective algorithm proposed in this paper defines three objectives. One of them is used as a restriction on the rules in order to obtain a Pareto front composed of a set of quite different rules with a high degree of coverage over the examples. The other two objectives take into account the support and the confidence of the rules. The use of the mentioned objective as restriction allows us the extraction of a set of rules which describe more complete information on most of the examples. Experimental evaluation of the algorithm, applying it to a market problem shows the validity of the proposal obtaining novel and valuable knowledge for the experts

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2007 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making

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