A correlation-based approach to attribute selection in chemical graph mining

被引：0
|
作者：
Okada, Takashi [1 ]
机构：
[1] Kwansei Gakuin Univ, Dept Informat, Sanda, Hyogo 6691337, Japan
来源：
NEW FRONTIERS IN ARTIFICIAL INTELLIGENCE | 2007年 / 3609卷
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The huge number of descriptive features is often a problem in data mining. We analyzed structure activity data for dopamine antagonists, which involves selecting useful features from numerous fragments extracted from their chemical structures. Correlation coefficients among categorical variables were used to select attributes. Chemists evaluated the rules obtained by the cascade model, and the importance of attribute selection was confirmed.
引用
收藏
页码：517 / 526
页数：10
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