Modeling Reuse on Case-Based Reasoning with Application to Breast Cancer Diagnosis
AIMSA '08 Proceedings of the 13th international conference on Artificial Intelligence: Methodology, Systems, and Applications
Fuzzy classification systems based on fuzzy information gain measures
Expert Systems with Applications: An International Journal
A Hybrid Higher Order Neural Classifier for handling classification problems
Expert Systems with Applications: An International Journal
Building a highly-compact and accurate associative classifier
Applied Intelligence
An innovative feature selection using fuzzy entropy
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part III
Feature evaluation and selection with cooperative game theory
Pattern Recognition
A novel feature selection method based on normalized mutual information
Applied Intelligence
Information Sciences: an International Journal
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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).