Genetic network programming with class association rule acquisition mechanisms from incomplete database

Kaoru Shimada*, Kotaro Hirasawa, Jinglu Hu

*この研究の対応する著者

研究成果: Conference contribution

5 被引用数 (Scopus)

抄録

A method of class association rule mining from incomplete databases is proposed using Genetic Network Programming (GNP). An incomplete database includes missing data in some tuples, however, the proposed method can extract important rules using these tuples. The proposed mechanisms can calculate measurements of association rules directly using GNP. GNP is one of the evolutionary optimization techniques, which uses the directed graph structure. Users can define the conditions of important rules flexibly and obtain enough number of important rules. Generally, it is not easy for Aprior-like methods to extract important rules from incomplete database. We have estimated the performances of the rule extraction and classification of the proposed method using incomplete data set. The results showed that the accuracy of classification of the proposed method is favorable even if some tuples include missing data.

本文言語English
ホスト出版物のタイトルSICE Annual Conference, SICE 2007
ページ2708-2714
ページ数7
DOI
出版ステータスPublished - 2007 12 1
イベントSICE(Society of Instrument and Control Engineers)Annual Conference, SICE 2007 - Takamatsu, Japan
継続期間: 2007 9 172007 9 20

出版物シリーズ

名前Proceedings of the SICE Annual Conference

Conference

ConferenceSICE(Society of Instrument and Control Engineers)Annual Conference, SICE 2007
国/地域Japan
CityTakamatsu
Period07/9/1707/9/20

ASJC Scopus subject areas

  • 制御およびシステム工学
  • コンピュータ サイエンスの応用
  • 電子工学および電気工学

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