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.
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© 2011 Springer-Verlag Berlin Heidelberg
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Xu, F., Pan, W., Wei, L., Du, H. (2011). An Efficient Fuzzy Rough Approach for Feature Selection. In: Yao, J., Ramanna, S., Wang, G., Suraj, Z. (eds) Rough Sets and Knowledge Technology. RSKT 2011. Lecture Notes in Computer Science(), vol 6954. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24425-4_15
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DOI: https://doi.org/10.1007/978-3-642-24425-4_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24424-7
Online ISBN: 978-3-642-24425-4
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