|Appears in Collections:||Computing Science and Mathematics Conference Papers and Proceedings|
|Peer Review Status:||Refereed|
|Title:||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. https://doi.org/10.1007/978-3-319-46562-3_19|
|Series/Report no.:||Advances in Intelligent Systems and Computing, 513|
|Conference Name:||UKCI 2016 - 16th UK Workshop on Computational Intelligence|
|Conference Dates:||2016-09-07 - 2016-09-09|
|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:||AM - Accepted Manuscript|
|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|
|evolutionary-algorithms-linkage (1).pdf||Fulltext - Accepted Version||3.48 MB||Adobe PDF||View/Open|
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