Swarm intelligence
Feature Selection for Support Vector Machines by Means of Genetic Algorithms
ICTAI '03 Proceedings of the 15th IEEE International Conference on Tools with Artificial Intelligence
Ant Colony Optimization
AIPR '04 Proceedings of the 33rd Applied Imagery Pattern Recognition Workshop
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Computational Methods of Feature Selection (Chapman & Hall/Crc Data Mining and Knowledge Discovery Series)
A new mutual information based measure for feature selection
Intelligent Data Analysis
ICDM'07 Proceedings of the 7th industrial conference on Advances in data mining: theoretical aspects and applications
A hybrid ant colony optimization for continuous domains
Expert Systems with Applications: An International Journal
An ant-inspired algorithm for detection of image edge features
Applied Soft Computing
Computers in Biology and Medicine
Self-adaptive differential evolution for feature selection in hyperspectral image data
Applied Soft Computing
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Feature selection is an important step in many pattern recognition systems that aims to overcome the so-called curse of dimensionality problem. Although Ant Colony Optimization (ACO) proved to be a powerful technique in different optimization problems, but it still needs some improvements when applied to the feature selection problem. This is due to the fact that it builds its solutions sequentially, where in feature selection this behavior will most likely not lead to the optimal solution. In this paper, a novel feature selection algorithm based on a combination of ACO and a simple, yet powerful, Differential Evolution (DE) operator is presented. The proposed combination enhances both the exploration and exploitation capabilities of the search procedure. The new algorithm is tested on two biosignal-driven applications. The performance of the proposed algorithm is compared with other dimensionality reduction techniques to prove its superiority.