An efficient fuzzy rough approach for feature selection

Authors:
Feifei Xu;Weiguo Pan;Lai Wei;Haizhou Du
Affiliations:
Shanghai University of Electric Power, Shanghai, China;Shanghai University of Electric Power, Shanghai, China;College of Information Engineering, Shanghai Maritime University, Shanghai, China;Shanghai University of Electric Power, Shanghai, China
Venue:
RSKT'11 Proceedings of the 6th international conference on Rough sets and knowledge technology
Year:
2011

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Abstract

Rough set theory is a powerful tool for feature selection. To avoid the information loss by discretization in rough sets, fuzzy rough sets are used to deal with the continuous values. However, the cost of computation of the approach is too high to be worked out as the number of selected features increases. In this paper, a new computational method is proposed to approximate the conditional mutual information between the selected features and the decision feature, and thus improve the efficiency and decrease the complexity of the classical fuzzy rough approach based on mutual information. Extensive experiments are conducted on the large-sized coal-fired power units dataset with steady state, and the experimental results confirm the efficiency and effectiveness of the proposed algorithm.