Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Correlation-based Feature Selection for Discrete and Numeric Class Machine Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Hybrid Genetic Algorithms for Feature Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
A hybrid genetic algorithm for feature selection wrapper based on mutual information
Pattern Recognition Letters
An efficient ant colony optimization approach to attribute reduction in rough set theory
Pattern Recognition Letters
Text feature selection using ant colony optimization
Expert Systems with Applications: An International Journal
The ANNIGMA-wrapper approach to fast feature selection for neuralnets
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Genetic programming for simultaneous feature selection and classifier design
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Objective functions for training new hidden units in constructive neural networks
IEEE Transactions on Neural Networks
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This paper presents a new hybrid genetic algorithm (HGA) for feature selection (FS) called as HGAFS. HGAFS incorporates a new local search operation that is devised and embedded in HGA to fine-tune the search in FS. The proposed local search operation works on basis of the distinct and informative nature of input features that is computed by their correlation information. The aim of using correlation information is to encourage the local search strategy for selecting less correlated (distinct) features. Such an encouragement reduces the redundancy of information in the generated subset of salient features. We have tested our methods on several real-world datasets and have compared the performances with the results of other existing algorithms. It is found that HGAFS produces consistently better performances.