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

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Part of the Lecture Notes in Computer Science book series (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.

Keywords

  • Brain Computer Interface
  • Mutual information
  • Evolutionary search
  • Iterated Local Search

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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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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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