Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/24559
Appears in Collections:Computing Science and Mathematics Conference Papers and Proceedings
Peer Review Status: Refereed
Authors: Adair, Jason
Brownlee, Alexander
Ochoa, Gabriela
Contact Email: sbr@cs.stir.ac.uk
Title: Evolutionary Algorithms with Linkage Information for Feature Selection in Brain Computer Interfaces
Editors: Angelov, P
Gegov, A
Jayne, C
Shen, Q
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 (ed.) Advances in Computational Intelligence Systems: Contributions Presented at the 16th UK Workshop on Computational Intelligence, September 7–9, 2016, Lancaster, UK, London: Springer. UKCI 2016 - 16th UK Workshop on Computational Intelligence, 7.9.2016 - 9.9.2016, Lancaster, pp. 287-307.
Issue Date: 7-Sep-2016
Series/Report no.: Advances in Intelligent Systems and Computing, 513
Conference Name: UKCI 2016 - 16th UK Workshop on Computational Intelligence
Conference Dates: 2016-09-07T00:00:00Z
Conference Location: Lancaster
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.
Status: Book Chapter: author post-print (pre-copy editing)
Rights: Publisher policy allows this work to be made available in this repository; The original publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-46562-3_19
URL: http://link.springer.com/chapter/10.1007/978-3-319-46562-3_19

Files in This Item:
File Description SizeFormat 
evolutionary-algorithms-linkage (1).pdf3.48 MBAdobe PDFView/Open



This item is protected by original copyright



Items in the Repository are protected by copyright, with all rights reserved, unless otherwise indicated.

If you believe that any material held in STORRE infringes copyright, please contact library@stir.ac.uk providing details and we will remove the Work from public display in STORRE and investigate your claim.