This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
Knowledge Acquisition Based on Rough Set Theory and Principal Component Analysis
March/April 2006 (vol. 21 no. 2)
pp. 78-85
An Zeng, Guangdong University of Technology and South China University of Technology
Dan Pan, Guandong Mobile Communication Co. Ltd.
Qi-Lun Zheng, South China University of Technology
Hong Peng, South China University of Technology

Rough set (RS) theory gives an approach to knowledge acquisition. Most RS-based reduction algorithms are to find minimal attribute reducts with heuristic knowledge derived only from the dependencies between condition attributes and decision attributes. The rules based on minimal attribute reducts are too simple to represent expert knowledge well. To acquire rules with stronger generalization capabilities, we advocate a knowledge acquisition approach based on RS and Principal Component Analysis (KA-RSPCA). KA-RSPCA uses a collective correlation coefficient as heuristic knowledge to assist attribute reduction and attribute value reduction. The coefficient is a PCA-based quantitative index to measure every condition attribute's contributions to the state space constructed by the whole of the condition attributes in a decision table.

For two test data sets in comparison with other algorithms, KA-RSPCA algorithm shows a reduction in error rate with only a slight increase in the number of rules used.

Index Terms:
collective correlation coefficient, principal component analysis, rough set, knowledge acquisition
Citation:
An Zeng, Dan Pan, Qi-Lun Zheng, Hong Peng, "Knowledge Acquisition Based on Rough Set Theory and Principal Component Analysis," IEEE Intelligent Systems, vol. 21, no. 2, pp. 78-85, March-April 2006, doi:10.1109/MIS.2006.32
Usage of this product signifies your acceptance of the Terms of Use.