Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/26525
Appears in Collections:Computing Science and Mathematics Conference Papers and Proceedings
Peer Review Status: Refereed
Author(s): Adair, Jason
Brownlee, Alexander
Ochoa, Gabriela
Contact Email: alexander.brownlee@stir.ac.uk
Title: Mutual Information Iterated Local Search: A Wrapper-Filter Hybrid for Feature Selection in Brain Computer Interfaces (Forthcoming)
Citation: Adair J, Brownlee A & Ochoa G (2018) Mutual Information Iterated Local Search: A Wrapper-Filter Hybrid for Feature Selection in Brain Computer Interfaces (Forthcoming) In: Proceedings of EvoStar 2018 (EvoApplications), Springer. EvoStar 2018, 4.4.2018 - 6.4.2018, Parma, Italy.
Issue Date: Apr-2018
Series/Report no.: Lecture Notes in Computer Science, 0302-9743
Conference Name: EvoStar 2018
Conference Dates: 2018-04-04T00:00:00Z
Conference Location: Parma, Italy
Abstract: Brain Computer Interfaces provide a very challenging classification task due to small numbers of instances, large numbers of features, non-stationary problems, and low signal-to-noise ratios. Feature selection (FS) is a promising solution to help mitigate these effects. Wrapper FS methods are typically found to outperform filter FS methods, but reliance on cross-validation accuracies can be misleading due to overfitting. This paper proposes a filter-wrapper hybrid based on Iterated Local Search and Mutual Information, and shows that it can provide more reliable solutions, where the solutions are more able to generalise to unseen data. This study further contributes comparisons over multiple datasets, something that has been uncommon in the literature.
Status: Book Chapter: author post-print (pre-copy editing)
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