Knowledge acquisition based on rough set theory and principal component analysis

被引：35
|
作者：
Zeng, A
Pan, D
Zheng, QL
Peng, H
机构：
[1] Guangdong Univ Technol, Fac Comp, Guangzhou 510006, Peoples R China
[2] S China Univ Technol, Sch Engn & Comp Sci, Guangzhou 510640, Peoples R China
[3] Guangdong Mobile Commun Co Ltd, Guangzhou 510100, Peoples R China
来源：
IEEE INTELLIGENT SYSTEMS | 2006年 / 21卷 / 02期
基金：
中国国家自然科学基金;
关键词：
D O I：
10.1109/MIS.2006.32
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
A knowledge acquisition algorithm (KA-RSPCA), based on rough set theory and principal component analysis, that increases the obtained rules' generalization power by integrating a measure of every condition attribute's condition contribution to the state space, is discussed. The process of this algorithms includes standardizing the variables, calculating the sample correlation matrix, and calculation of principal components. KA-RSPCA is apt to acquire a reduct consisting of more important contributes with a larger contribution. This algorithm is claimed to be a meaningful procedure for studying knowledge acquisition and applying it to business intelligence in various industries.
引用
收藏
页码：78 / 85
页数：8
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