Prescreening of candidate rules using association rule mining and Pareto-optimality in genetic rule selection

被引：0

作者：

Ishibuchi, Hisao ^{[1
]}

Kuwajima, Isao ^{[1
]}

Nojima, Yusuke ^{[1
]}

机构：

[1] Osaka Prefecture Univ, Grad Sch Engn, Dept Comp Sci & Intelligent Syst, Naka Ku, 1-1 Gakuen Cho, Osaka 5998531, Japan

来源：

KNOWLEDGE-BASED INTELLIGENT INFORMATION AND ENGINEERING SYSTEMS: KES 2007 - WIRN 2007, PT II, PROCEEDINGS | 2007年 / 4693卷

关键词：

data mining; classifier design; genetic rule selection; evolutionary multiobjective optimization; multiobjective machine learning;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Genetic rule selection is an approach to the design of classifiers with high accuracy and high interpretability. It searches for a small number of simple classification rules from a large number of candidate rules. The effectiveness of genetic rule selection strongly depends on the choice of candidate rules. If we have hundreds of thousands of candidate rules, it is very difficult to efficiently search for their good subsets. On the other hand, if we have only a few candidate rules, rule selection does not make sense. In this paper, we examine the use of Pareto-optimal and near Pareto-optimal rules with respect to support and confidence as candidate rules in genetic rule selection.

引用

页码：509 / 516

页数：8

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