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Knowledge acquisition based on rough set theory and principal component analysis

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4 Author(s)
An Zeng ; Guangdong Univ. of Technol., Guangzhou, China ; Dan Pan ; Qi-lun Zheng ; Hong Peng

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.

Published in:

Intelligent Systems, IEEE  (Volume:21 ,  Issue: 2 )