Feature Subset Selection Using Differential Evolution

Publisher:
Springer
Publication Type:
Chapter
Citation:
Advances in Neuro-Information Processing - Lecture Notes in Computer Science, 2009, First Edition, pp. 103 - 110
Issue Date:
2009-01
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One of the fundamental motivations for feature selection is to overcome the curse of dimensionality. A novel feature selection algorithm is developed in this chapter based on a combination of Differential Evolution (DE) optimization technique and statistical feature distribution measures. The new algorithm, referred to as DEFS, utilizes the DE float number optimizer in a combinatorial optimization problem like feature selection. The proposed DEFS highly reduces the computational cost while at the same time proves to present a powerful performance. The DEFS is tested as a search procedure on different datasets with varying dimensionality. Practical results indicate the significance of the proposed DEFS in terms of solutions optimality and memory requirements.
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