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Leveraging cooperation for parallel multi‐objective feature selection in high‐dimensional EEG data
Author(s) -
Kimovski Dragi,
Ortega Julio,
Ortiz Andrés,
Baños Raúl
Publication year - 2015
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.3594
Subject(s) - computer science , speedup , curse of dimensionality , feature selection , machine learning , artificial intelligence , quality (philosophy) , feature (linguistics) , selection (genetic algorithm) , dimensionality reduction , data mining , parallel computing , linguistics , epistemology , philosophy
Summary Bioinformatics applications frequently involve high‐dimensional model building or classification problems that require reducing dimensionality to improve learning accuracy while irrelevant inputs are removed. Thus, feature selection has become an important issue on these applications. Moreover, several approaches for supervised and unsupervised feature selections as a multi‐objective optimization problem have been recently proposed to cope with issues on performance evaluation of classifiers and models. As parallel processing constitutes an important tool to reach efficient approaches that make it possible to tackle complex problems within reasonable computing times, in this paper, alternatives for the cooperation of subpopulations in multi‐objective evolutionary algorithms have been identified and classified, and several procedures have been implemented and evaluated on some synthetic and Brain–Computer Interface datasets. The results show different improvements achieved in the solution quality and speedups, depending on the cooperation alternative and dataset. We show alternatives that even provide superlinear speedups with only small reductions in the solution quality, besides another cooperation alternative that improves the quality of the solutions with speedups similar to, or only slightly higher than, the speedup obtained by the parallel fitness evaluation in a master‐worker implementation (the alternative used as reference that behaves as the corresponding sequential multi‐objective approach). Copyright © 2015 John Wiley & Sons, Ltd.