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Evolutionary Algorithms with Linkage Information for Feature Selection in Brain Computer Interfaces

Citation
Adair J, Brownlee A & Ochoa G (2016) Evolutionary Algorithms with Linkage Information for Feature Selection in Brain Computer Interfaces. In: Angelov P, Gegov A, Jayne C & Shen Q (eds.) Advances in Computational Intelligence Systems: Contributions Presented at the 16th UK Workshop on Computational Intelligence, September 7–9, 2016, Lancaster, UK. Advances in Intelligent Systems and Computing, 513. UKCI 2016 - 16th UK Workshop on Computational Intelligence, Lancaster, 07.09.2016-09.09.2016. London: Springer, pp. 287-307. http://link.springer.com/chapter/10.1007/978-3-319-46562-3_19; https://doi.org/10.1007/978-3-319-46562-3_19

Abstract
Abstract Brain Computer Interfaces are an essential technology for the advancement of prosthetic limbs, but current signal acquisition methods are hindered by a number of factors, not least, noise. In this context, Feature Selection is required to choose the important signal features and improve classifier accuracy. Evolutionary algorithms have proven to outperform filtering methods (in terms of accuracy) for Feature Selection. This paper applies a single-point heuristic search method, Iterated Local Search (ILS), and compares it to a genetic algorithm (GA) and a memetic algorithm (MA). It then further attempts to utilise Linkage between features to guide search operators in the algorithms stated. The GA was found to outperform ILS. Counter-intuitively, linkage-guided algorithms resulted in higher classification error rates than their unguided alternatives. Explanations for this are explored.

Keywords
evolutionary search; brain computer interfaces; Iterated Local Search; Genetic Algorithms; Feature Selection; Intelligent Operators; feature selection; memetic algorithms; linkage score; linkage detection algorithms; epistasis; eeg; prosthetics

StatusPublished
Author(s)Adair, Jason; Brownlee, Alexander; Ochoa, Gabriela
Title of seriesAdvances in Intelligent Systems and Computing
Number in series513
Publication date07/09/2016
Publication date online30/09/2016
URLhttp://hdl.handle.net/1893/24559
Related URLshttp://wp.lancs.ac.uk/ukci2016/
PublisherSpringer
Publisher URLhttp://link.springer.com/…3-319-46562-3_19
Place of publicationLondon
ISSN of series2194-5357
ISBN978-3-319-46561-6
eISBN978-3-319-46562-3
ConferenceUKCI 2016 - 16th UK Workshop on Computational Intelligence
Conference locationLancaster
Dates
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