A new multi-objective wrapper method for feature selection - Accuracy and stability analysis for BCI

NEUROCOMPUTING

卷:333

Article

Gonzalez, Jesus¹  Ortega, Julio¹  Damas, Miguel¹  Martin-Smith, Pedro¹  Gan, John Q.² 

[1] Univ Granada, Dept Comp Architecture & Technol, CITIC, Granada, Spain

[2] Univ Essex, Sch Comp Sci & Elect Engn, Colchester, Essex, England

关键词: BCI;    EEG;    Motor imagery;    Feature selection;    Multi-objective problem;    Evolutionary algorithm;    Classification;    Stability;    Ensemble;   

DOI  :  10.1016/j.neucom.2019.01.017

来源: Elsevier

【 摘 要 】

Feature selection is an important step in building classifiers for high-dimensional data problems, such as EEG classification for BCI applications. This paper proposes a new wrapper method for feature selection, based on a multi-objective evolutionary algorithm, where the representation of the individuals or potential solutions, along with the breeding operators and objective functions, have been carefully designed to select a small subset of features that has good generalization capability, trying to avoid the over-fitting problems that wrapper methods usually suffer. A novel feature ranking procedure is also proposed in order to analyze the stability of the proposed wrapper method. Four different classification schemes have been applied within the proposed wrapper method in order to evaluate its accuracy and stability for feature selection on a real motor imagery dataset. Experimental results show that the wrapper method presented in this paper is able to obtain very small subsets of features, which are quite stable and also achieve high classification accuracy, regardless of the classifiers used. Crown Copyright (C) 2019 Published by Elsevier B.V. All rights reserved.

【 授权许可】

Free