A fuzzy neural network for pattern classification and feature selection
Fuzzy Sets and Systems
IEEE Intelligent Systems
Information-preserving hybrid data reduction based on fuzzy-rough techniques
Pattern Recognition Letters
Feature selection in robust clustering based on Laplace mixture
Pattern Recognition Letters
FS_SFS: A novel feature selection method for support vector machines
Pattern Recognition
Expert Systems with Applications: An International Journal
Computer Methods and Programs in Biomedicine
Expert Systems with Applications: An International Journal
An efficient fuzzy classifier with feature selection based on fuzzyentropy
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
OFFSS: optimal fuzzy-valued feature subset selection
IEEE Transactions on Fuzzy Systems
Fuzzy-Rough Sets Assisted Attribute Selection
IEEE Transactions on Fuzzy Systems
Unsupervised feature evaluation: a neuro-fuzzy approach
IEEE Transactions on Neural Networks
Input feature selection for classification problems
IEEE Transactions on Neural Networks
A neuro-fuzzy scheme for simultaneous feature selection and fuzzy rule-based classification
IEEE Transactions on Neural Networks
Feature selection in MLPs and SVMs based on maximum output information
IEEE Transactions on Neural Networks
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The effects of linguistic hedges (LHs) on neuro-fuzzy classifier are shown in Part 1. This paper presents a fuzzy feature selection (FS) method based on the LH concept. The values of LHs can be used to show the importance degree of fuzzy sets. When this property is used for classification problems, and every class is defined by a fuzzy classification rule, the LHs of every fuzzy set denote the importance degree of input features. If the LHs values of features are close to concentration values, these features are more important or relevant, and can be selected. On the contrary, if the LH values of features are close to dilation values, these features are not important, and can be eliminated. According to the LHs value of features, the redundant, noisily features can be eliminated, and significant features can be selected. For this aim, a new LH-based FS algorithm is proposed by using adaptive neuro-fuzzy classifier (ANFC). In this study, the meanings of LHs are used to determine the relevant and irrelevant features of real-world databases. The experimental studies are shown the success of using the LHs in FS algorithm.