Differential Evolution based Feature Subset Selection

Publication Type:
Conference Proceeding
Proceedings of the 19th International Conference on Pattern Recognition (ICPR-2008), 2008, pp. 1 - 4
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In this paper, a novel feature selection algorithm based on differential evolution (DE) optimization technique is presented. The new algorithm, called DEFS, modifies the DE which is a real-valued optimizer, to suit the problem of feature selection. The proposed DEFS highly reduces the computational costs while at the same time proving to present powerful performance. The DEFS technique is applied to a brain-computer-interface (BCI) application and compared with other dimensionality reduction techniques. The practical results indicate the significance of the proposed algorithm in terms of solutions optimality, memory requirement, and computational cost.
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