A Rule-Based Classification System Enhanced by Multi-Objective Genetic Algorithm

被引：0

作者：

Azakami, Kenzoh ^{[1
]}

Mabu, Shingo ^{[2
]}

Obayashi, Masanao ^{[2
]}

Kuremoto, Takashi ^{[2
]}

机构：

[1] Yamaguchi Univ, Grad Sch Sci & Engn, Tokiwadai 2-6-11, Ube, Yamaguchi 7558611, Japan

[2] Yamaguchi Univ, Grad Sch Sci & Technol Innovat, Tokiwadai 2-6-11, Ube, Yamaguchi 7558611, Japan

来源：

ICAROB 2017: PROCEEDINGS OF THE 2017 INTERNATIONAL CONFERENCE ON ARTIFICIAL LIFE AND ROBOTICS | 2017年

关键词：

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：

Recent years, data mining techniques have been developed for extracting rules from big data. However, there are some problems to be considered, for example, it is difficult to judge which rules are important and which are not important; and even in simple classification problems with the small number of classes, a various sub-patterns to be considered potentially exist in each class. To solve the above problems, a rule clustering algorithm using multi-objective genetic algorithm is proposed.

引用

页码：P650 / P653

页数：4

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