Issue No. 02 - March/April (2006 vol. 21)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/MIS.2006.32
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
<p>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.</p><p>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.</p>
collective correlation coefficient, principal component analysis, rough set, knowledge acquisition
D. Pan, H. Peng, Q. Zheng and A. Zeng, "Knowledge Acquisition Based on Rough Set Theory and Principal Component Analysis," in IEEE Intelligent Systems, vol. 21, no. , pp. 78-85, 2006.