Hybrid Genetic Algorithms for Feature Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
AIPR '04 Proceedings of the 33rd Applied Imagery Pattern Recognition Workshop
IEEE Intelligent Systems
Improved binary PSO for feature selection using gene expression data
Computational Biology and Chemistry
Feature selection based-on genetic algorithm for image annotation
Knowledge-Based Systems
Text feature selection using ant colony optimization
Expert Systems with Applications: An International Journal
Dimensionality reduction using genetic algorithms
IEEE Transactions on Evolutionary Computation
Fast generic selection of features for neural network classifiers
IEEE Transactions on Neural Networks
Optimized distance metrics for differential evolution based nearest prototype classifier
Expert Systems with Applications: An International Journal
Efficient feature selection filters for high-dimensional data
Pattern Recognition Letters
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Feature subset selection using improved binary gravitational search algorithm
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
Hi-index | 12.06 |
One of the fundamental motivations for feature selection is to overcome the curse of dimensionality problem. This paper presents a novel feature selection method utilizing a combination of differential evolution (DE) optimization method and a proposed repair mechanism based on feature distribution measures. The new method, abbreviated as DEFS, utilizes the DE float number optimizer in the combinatorial optimization problem of feature selection. In order to make the solutions generated by the float-optimizer suitable for feature selection, a roulette wheel structure is constructed and supplied with the probabilities of features distribution. These probabilities are constructed during iterations by identifying the features that contribute to the most promising solutions. The proposed DEFS is used to search for optimal subsets of features in datasets with varying dimensionality. It is then utilized to aid in the selection of Wavelet Packet Transform (WPT) best basis for classification problems, thus acting as a part of a feature extraction process. Practical results indicate the significance of the proposed method in comparison with other feature selection methods.