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A new multi-objective wrapper method for feature selection – Accuracy and stability analysis for BCI
- Source :
- Digibug. Repositorio Institucional de la Universidad de Granada, instname
- Publication Year :
- 2019
- Publisher :
- Elsevier BV, 2019.
-
Abstract
- 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.<br />Project TIN2015-67020-P (Spanish “Ministerio de Economía y Competitividad”)<br />European Regional Development Funds (ERDF)
- Subjects :
- 0209 industrial biotechnology
Computer science
Generalization
Cognitive Neuroscience
Evolutionary algorithm
Stability (learning theory)
Feature selection
02 engineering and technology
Motor imagery
020901 industrial engineering & automation
Artificial Intelligence
0202 electrical engineering, electronic engineering, information engineering
EEG
BCI
Representation (mathematics)
Brain–computer interface
business.industry
Pattern recognition
Classification
Computer Science Applications
Multi-objective problem
020201 artificial intelligence & image processing
Artificial intelligence
business
Stability
Ensemble
Subjects
Details
- ISSN :
- 09252312
- Volume :
- 333
- Database :
- OpenAIRE
- Journal :
- Neurocomputing
- Accession number :
- edsair.doi.dedup.....e67428f8ade5ffaca55028d52ce82d7c