Unsupervised Feature Selection Using Feature Similarity
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature selection with neural networks
Pattern Recognition Letters
Improving fuzzy c-means clustering based on feature-weight learning
Pattern Recognition Letters
Feature Subset Selection and Ranking for Data Dimensionality Reduction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fuzzy feature selection based on min-max learning rule and extension matrix
Pattern Recognition
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Expert Systems with Applications: An International Journal
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A Hybrid Higher Order Neural Classifier for handling classification problems
Expert Systems with Applications: An International Journal
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IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Measures for unsupervised fuzzy-rough feature selection
International Journal of Hybrid Intelligent Systems - Advances in Intelligent Agent Systems
Investigating a novel GA-based feature selection method using improved KNN classifiers
International Journal of Information and Communication Technology
Neuro-fuzzy methodology for selecting genes mediating lung cancer
PReMI'11 Proceedings of the 4th international conference on Pattern recognition and machine intelligence
A new method for feature selection
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
A new fuzzy segmentation approach based on S-FCM type 2 using LBP-GCO features
Image Communication
Unsupervised feature selection in digital mammogram image using rough set theory
International Journal of Bioinformatics Research and Applications
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Information Sciences: an International Journal
International Journal of Data Warehousing and Mining
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Computers and Electrical Engineering
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Demonstrates a way of formulating neuro-fuzzy approaches for both feature selection and extraction under unsupervised learning. A fuzzy feature evaluation index for a set of features is defined in terms of degree of similarity between two patterns in both the original and transformed feature spaces. A concept of flexible membership function incorporating weighted distance is introduced for computing membership values in the transformed space. Two new layered networks are designed. The tasks of membership computation and minimization of the evaluation index, through unsupervised learning process, are embedded into them without requiring the information on the number of clusters in the feature space. The network for feature selection results in an optimal order of individual importance of the features. The other one extracts a set of optimum transformed features, by projecting n-dimensional original space directly to n'-dimensional (n'