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PAKDD '02 Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
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A modified PNN algorithm with optimal PD modeling using the orthogonal least squares method
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A novel feature selection method for large-scale data sets
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AIAP'07 Proceedings of the 25th conference on Proceedings of the 25th IASTED International Multi-Conference: artificial intelligence and applications
An efficient bit-based feature selection method
Expert Systems with Applications: An International Journal
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Expert Systems with Applications: An International Journal
Medical data mining by fuzzy modeling with selected features
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Expert Systems with Applications: An International Journal
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IFSA '07 Proceedings of the 12th international Fuzzy Systems Association world congress on Foundations of Fuzzy Logic and Soft Computing
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Expert Systems with Applications: An International Journal
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Expert Systems with Applications: An International Journal
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Expert Systems with Applications: An International Journal
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The effect of linguistic hedges on feature selection: Part 2
Expert Systems with Applications: An International Journal
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Expert Systems with Applications: An International Journal
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Expert Systems with Applications: An International Journal
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AI'05 Proceedings of the 18th Australian Joint conference on Advances in Artificial Intelligence
Feature evaluation and selection with cooperative game theory
Pattern Recognition
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A hierarchical approach to multi-class fuzzy classifiers
Expert Systems with Applications: An International Journal
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Information Fusion
A threshold fuzzy entropy based feature selection for medical database classification
Computers in Biology and Medicine
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This paper presents an efficient fuzzy classifier with the ability of feature selection based on a fuzzy entropy measure. Fuzzy entropy is employed to evaluate the information of pattern distribution in the pattern space. With this information, we can partition the pattern space into nonoverlapping decision regions for pattern classification. Since the decision regions do not overlap, both the complexity and computational load of the classifier are reduced and thus the training time and classification time are extremely short. Although the decision regions are partitioned into nonoverlapping subspaces, we can achieve good classification performance since the decision regions can be correctly determined via our proposed fuzzy entropy measure. In addition, we also investigate the use of fuzzy entropy to select relevant features. The feature selection procedure not only reduces the dimensionality of a problem but also discards noise-corrupted, redundant and unimportant features. Finally, we apply the proposed classifier to the Iris database and Wisconsin breast cancer database to evaluate the classification performance. Both of the results show that the proposed classifier can work well for the pattern classification application