Medical association rule mining using genetic network programming

Kaoru Shimada, Rouchen Wang, Kotaro Hirasawa, Takayuki Furuzuki

研究成果: Article査読

3 被引用数 (Scopus)

抄録

An efficient algorithm for building a classifier is proposed based on an important association rule mining using Genetic Network Programming (GNP). The proposed method measures the significance of the association via the chi-squared test. Users can define the conditions of important association rules for building a classifier flexibly. The definition can include not only the minimum threshold chi-squared value, but also the number of attributes in the association rules. Therefore, all the extracted important rules can be used for classification directly. GNP is one of the evolutionary optimization techniques, which uses the directed graph structure as genes. Instead of generating a large number of candidate rules, our method can obtain a sufficient number of important association rules for classification. In addition, our method suits association rule mining from dense databases such as medical datasets, where many frequently occurring items are found in each tuple. In this paper, we describe an algorithm for classification using important association rules extracted by GNP with acquisition mechanisms and present some experimental results of medical datasets.

本文言語English
ページ(範囲)849-856
ページ数8
ジャーナルIEEJ Transactions on Electronics, Information and Systems
126
7
DOI
出版ステータスPublished - 2006

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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