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Mutual Information Iterated Local Search: A Wrapper-Filter Hybrid for Feature Selection in Brain Computer Interfaces

Citation
Adair J, Brownlee A & Ochoa G (2018) Mutual Information Iterated Local Search: A Wrapper-Filter Hybrid for Feature Selection in Brain Computer Interfaces. In: Applications of Evolutionary Computation. EvoApplications 2018. Lecture Notes in Computer Science, 10784. EvoStar 2018, Parma, Italy, 04.04.2018-06.04.2018. Cham, Switzerland: Springer, pp. 63-77. https://link.springer.com/chapter/10.1007/978-3-319-77538-8_5; https://doi.org/10.1007/978-3-319-77538-8_5

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.

Keywords
Brain Computer Interface; Mutual Information; Evolutionary Search; Iterated Local Search

StatusPublished
Author(s)Adair, Jason; Brownlee, Alexander; Ochoa, Gabriela
Title of seriesLecture Notes in Computer Science
Number in series10784
Publication date31/12/2018
Publication date online08/03/2018
URLhttp://hdl.handle.net/1893/26534
Related URLshttp://www.evostar.org/2018/
PublisherSpringer
Publisher URLhttps://link.springer.com/…-3-319-77538-8_5
Place of publicationCham, Switzerland
eISSN1611-3349
ISSN of series0302-9743
ISBN978-3-319-77537-1
eISBN978-3-319-77538-8
ConferenceEvoStar 2018
Conference locationParma, Italy
Dates
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