|Appears in Collections:||Computing Science and Mathematics Conference Papers and Proceedings|
|Peer Review Status:||Refereed|
|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.|
|Series/Report no.:||Lecture Notes in Computer Science, 0302-9743|
|Conference Name:||EvoStar 2018|
|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)|
|Rights:||This item has been embargoed for a period. During the embargo please use the Request a Copy feature at the foot of the Repository record to request a copy directly from the author. You can only request a copy if you wish to use this work for your own research or private study.|
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