A framework of cooperating intrusion detection based on clustering analysis and expert system
InfoSecu '04 Proceedings of the 3rd international conference on Information security
Information-preserving hybrid data reduction based on fuzzy-rough techniques
Pattern Recognition Letters
Fuzzy feature selection based on min-max learning rule and extension matrix
Pattern Recognition
Similarity-Based Feature Selection for Learning from Examples with Continuous Values
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Attribute selection with fuzzy decision reducts
Information Sciences: an International Journal
The effect of linguistic hedges on feature selection: Part 2
Expert Systems with Applications: An International Journal
An innovative feature selection using fuzzy entropy
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part III
An accumulative points/votes based approach for feature selection
CIARP'11 Proceedings of the 16th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Halfway through the semantic gap: Prosemantic features for image retrieval
Information Sciences: an International Journal
Automatic categorisation of comments in social news websites
Expert Systems with Applications: An International Journal
Opcode sequences as representation of executables for data-mining-based unknown malware detection
Information Sciences: an International Journal
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Feature subset selection is a well-known pattern recognition problem, which aims to reduce the number of features used in classification or recognition. This reduction is expected to improve the performance of classification algorithms in terms of speed, accuracy and simplicity. Most existing feature selection investigations focus on the case that the feature values are real or nominal, very little research is found to address the fuzzy-valued feature subset selection and its computational complexity. This paper focuses on a problem called optimal fuzzy-valued feature subset selection (OFFSS), in which the quality-measure of a subset of features is defined by both the overall overlapping degree between two classes of examples and the size of feature subset. The main contributions of this paper are that: 1) the concept of fuzzy extension matrix is introduced; 2) the computational complexity of OFFSS is proved to be NP-hard; 3) a simple but powerful heuristic algorithm for OFFSS is given; and 4) the feasibility and simplicity of the proposed algorithm are demonstrated by applications of OFFSS to fuzzy decision tree induction and by comparisons with three different feature selection techniques developed recently.