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

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Applications of Evolutionary Computation (EvoApplications 2018)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10784))

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 over-fitting. 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.

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Notes

  1. 1.

    http://www.bbci.de/competition/ii/#datasets.

  2. 2.

    http://www.bsp.brain.riken.jp/~qibin/homepage/Datasets.html.

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Acknowledgements

Work funded by UK EPSRC grant EP/J017515 (DAASE).

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Authors and Affiliations

  1. Computing Science and Mathematics, University of Stirling, Stirling, UK

    Jason Adair, Alexander E. I. Brownlee & Gabriela Ochoa

Authors
  1. Jason Adair
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  2. Alexander E. I. Brownlee
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  3. Gabriela Ochoa
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Correspondence to Jason Adair .

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Editors and Affiliations

  1. Edinburgh Napier University, Edinburgh, United Kingdom

    Kevin Sim

  2. Johannes Gutenberg University of Mainz, Mainz, Germany

    Paul Kaufmann

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Adair, J., Brownlee, A.E.I., Ochoa, G. (2018). Mutual Information Iterated Local Search: A Wrapper-Filter Hybrid for Feature Selection in Brain Computer Interfaces. In: Sim, K., Kaufmann, P. (eds) Applications of Evolutionary Computation. EvoApplications 2018. Lecture Notes in Computer Science(), vol 10784. Springer, Cham. https://doi.org/10.1007/978-3-319-77538-8_5

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  • DOI: https://doi.org/10.1007/978-3-319-77538-8_5

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-319-77538-8

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