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Erschienen in:
2009 | OriginalPaper | Buchkapitel
Feature Subset Selection Using Differential Evolution
verfasst von : Rami N. Khushaba, Ahmed Al-Ani, Adel Al-Jumaily
Erschienen in: Advances in Neuro-Information Processing
Verlag: Springer Berlin Heidelberg
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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.