A supervised filter method for multi-objective feature selection in EEG classification based on multi-resolution analysis for BCI

This paper proposes a supervised filter method for evolutionary multi-objective feature selection for classification problems in high-dimensional feature space, which is evaluated by comparison with wrapper approaches for the same application. The filter method based on a set of label-aided utility...

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Veröffentlicht in:	Neurocomputing (Amsterdam) Jg. 250; S. 45 - 56
Hauptverfasser:	Martín-Smith, Pedro, Ortega, Julio, Asensio-Cubero, Javier, Gan, John Q., Ortiz, Andrés
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier B.V 09.08.2017
Schlagworte:	Brain–computer interfaces (BCI) Feature selection Filter methods Multi-objective optimization Multi-resolution analysis (MRA) Wrapper-based feature selection Filter methods Brain–computer interfaces (BCI) Feature selection Multi-objective optimization Multi-resolution analysis (MRA) Wrapper-based feature selection
ISSN:	0925-2312, 1872-8286
Online-Zugang:	Volltext
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