Class association rule mining with chi-squared test using Genetic Network Programming

Kaoru Shimada*, Kotaro Hirasawa, Jinglu Hu

*この研究の対応する著者

研究成果: Conference contribution

64 被引用数 (Scopus)

抄録

An efficient algorithm for important class association rule mining using Genetic Network Programming (GNP) is proposed. GNP is one of the evolutionary optimization techniques, which uses directed graph structures as genes. Instead of generating a large number of candidate rules, the method can obtain a sufficient number of important association rules for classification. The proposed method measures the significance of the association via the chi-squared test. Therefore, all the extracted important rules can be used for classification directly. In addition, the method suits class association rule mining from dense databases, where many frequently occurring items are found in each tuple. Users can define conditions of extracting important class association rules. In this paper, we describe an algorithm for class association rule mining with chi-squared test using GNP and present a classifier using these extracted rules.

本文言語English
ホスト出版物のタイトル2006 IEEE International Conference on Systems, Man and Cybernetics
出版社Institute of Electrical and Electronics Engineers Inc.
ページ5338-5344
ページ数7
ISBN(印刷版)1424401003, 9781424401000
DOI
出版ステータスPublished - 2006 1 1
イベント2006 IEEE International Conference on Systems, Man and Cybernetics - Taipei, Taiwan, Province of China
継続期間: 2006 10 82006 10 11

出版物シリーズ

名前Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
6
ISSN(印刷版)1062-922X

Conference

Conference2006 IEEE International Conference on Systems, Man and Cybernetics
国/地域Taiwan, Province of China
CityTaipei
Period06/10/806/10/11

ASJC Scopus subject areas

  • 工学(全般)

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