Feature subset selection based on fuzzy entropy measures for handling classification problems |
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Authors: | Jen-Da Shie Shyi-Ming Chen |
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Affiliation: | (1) Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, 43, Section 4, Keelung Road, Taipei, 106, Taiwan |
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Abstract: | In this paper, we present a new method for dealing with feature subset selection based on fuzzy entropy measures for handling
classification problems. First, we discretize numeric features to construct the membership function of each fuzzy set of a
feature. Then, we select the feature subset based on the proposed fuzzy entropy measure focusing on boundary samples. The
proposed method can select relevant features to get higher average classification accuracy rates than the ones selected by
the MIFS method (Battiti, R. in IEEE Trans. Neural Netw. 5(4):537–550, 1994), the FQI method (De, R.K., et al. in Neural Netw. 12(10):1429–1455, 1999), the OFEI method, Dong-and-Kothari’s method (Dong, M., Kothari, R. in Pattern Recognit. Lett. 24(9):1215–1225, 2003) and the OFFSS method (Tsang, E.C.C., et al. in IEEE Trans. Fuzzy Syst. 11(2):202–213, 2003).
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Keywords: | Fuzzy entropy Classification problems Feature subset selection Fuzzy logic Membership grade |
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