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research
Differential Evolution based feature subset selection
Authors
A Al-Ani
A Al-Jumaily
RN Khushaba
Publication date
1 December 2008
Publisher
Doi
Cite
Abstract
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. © 2008 IEEE
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Last time updated on 01/04/2019
OPUS - University of Technology Sydney
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oai:opus.lib.uts.edu.au:10453/...
Last time updated on 14/09/2015