Estimating attributes: analysis and extensions of RELIEF
ECML-94 Proceedings of the European conference on machine learning on Machine Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised feature selection using a neuro-fuzzy approach
Pattern Recognition Letters
Unsupervised Feature Selection Using Feature Similarity
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature Selection for Knowledge Discovery and Data Mining
Feature Selection for Knowledge Discovery and Data Mining
An adaptation of Relief for attribute estimation in regression
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Feature Selection Algorithms: A Survey and Experimental Evaluation
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
An introduction to variable and feature selection
The Journal of Machine Learning Research
Toward Integrating Feature Selection Algorithms for Classification and Clustering
IEEE Transactions on Knowledge and Data Engineering
Feature Subset Selection and Ranking for Data Dimensionality Reduction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Identifying critical variables of principal components for unsupervised feature selection
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
OFFSS: optimal fuzzy-valued feature subset selection
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Neural Networks
Unsupervised feature evaluation: a neuro-fuzzy approach
IEEE Transactions on Neural Networks
Similarity-margin based feature selection for symbolic interval data
Pattern Recognition Letters
Spatial distance join based feature selection
Engineering Applications of Artificial Intelligence
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In many systems, such as fuzzy neural network, we often adopt the language labels (such as large, medium, small, etc.) to split the original feature into several fuzzy features. In order to reduce the computation complexity of the system after the fuzzification of features, the optimal fuzzy feature subset should be selected. In this paper, we propose a new heuristic algorithm, where the criterion is based on min-max learning rule and fuzzy extension matrix is designed as the search strategy. The algorithm is proved in theory and has shown its high performance over several real-world benchmark data sets.