Medical Association Rule Mining Using Genetic Network Programming
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- Shimada Kaoru
- Graduate School of Information, Production and Systems, Waseda University
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- Wang Rouchen
- Graduate School of Information, Production and Systems, Waseda University
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- Hirasawa Kotaro
- Graduate School of Information, Production and Systems, Waseda University
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- Furuzuki Takayuki
- Graduate School of Information, Production and Systems, Waseda University
Bibliographic Information
- Other Title
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- 遺伝的ネットワークプログラミングを用いた医療相関ルールの抽出
- イデンテキ ネットワーク プログラミング オ モチイタ イリョウ ソウカン ルール ノ チュウシュツ
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Abstract
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.
Journal
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- IEEJ Transactions on Electronics, Information and Systems
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IEEJ Transactions on Electronics, Information and Systems 126 (7), 849-856, 2006
The Institute of Electrical Engineers of Japan
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Keywords
Details 詳細情報について
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- CRID
- 1390282679582666624
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- NII Article ID
- 10018146454
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- NII Book ID
- AN10065950
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- ISSN
- 13488155
- 03854221
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- NDL BIB ID
- 8022513
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- Text Lang
- ja
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- Data Source
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- JaLC
- NDL
- Crossref
- CiNii Articles
- KAKEN
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- Abstract License Flag
- Disallowed