書誌事項
- タイトル別名
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- Medical Association Rule Mining Using Genetic Network Programming
- 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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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.
収録刊行物
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- 電気学会論文誌Ｃ（電子・情報・システム部門誌）
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電気学会論文誌Ｃ（電子・情報・システム部門誌） 126 (7), 849-856, 2006
一般社団法人 電気学会
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詳細情報 詳細情報について
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- CRID
- 1390282679582666624
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- NII論文ID
- 10018146454
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- NII書誌ID
- AN10065950
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- ISSN
- 13488155
- 03854221
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- NDL書誌ID
- 8022513
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- 本文言語コード
- ja
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- データソース種別
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- JaLC
- NDL
- Crossref
- CiNii Articles
- KAKEN
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- 抄録ライセンスフラグ
- 使用不可