Knowledge Acquisition Based on Rough Set Theory and Principal Component Analysis
An Zeng, Dan Pan, Qi-Lun Zheng, Hong Peng · IEEE Intelligent Systems · 2006
In this paper, we've developed a novel approach to knowledge acquisition based on rough set theory and principal component analysis. A PCA-based quantitative index measures the relative importance of different condition attributes among the state space constructed by all condition attributes. The index strengthens the attribute and attribute-value reductions while maintaining the decision table's discernibility relations. Our KA-RSPCA algorithm outperformed four other RS algorithms on two test data sets.