Paper Info
Title
A Label-Aided Filter Method for Multi-objective Feature Selection in EEG Classification for BCI.
Abstract
This paper proposes and evaluates a filter approach for evolutionary multi-objective feature selection in classification problems with a large number of features. Such classification problems frequently appear in many bioinformatics applications where the number of patterns is smaller than the number of features and thus the curse of dimensionality problem exists. The main contribution of this paper is proposing a set of label-aided utility functions that allows the effective search of the most adequate subset of features through an evolutionary multi-objective optimization scheme. The experimental results have been obtained in a brain-computer interface (BCI) classification task based on LDA classifiers, where the properties of multi-resolution analysis (MRA) for signal analysis in temporal and spectral domains have been used to extract the features from EEG signals. The results from the proposed filter method demonstrate some advantages such as less time consumption and better generalization capabilities with respect to some wrapper-based multi-objective feature selection alternatives.
Year
DOI
Venue
2015
10.1007/978-3-319-19258-1_12
ADVANCES IN COMPUTATIONAL INTELLIGENCE, PT I (IWANN 2015)
Keywords
Field
DocType
Brain-Computer Interfaces (BCI),Filter methods,Feature selection,Multi-objective optimization,Multi-Resolution Analysis (MRA)
Signal processing,Feature selection,Eeg classification,Pattern recognition,Computer science,Brain–computer interface,Multi-objective optimization,Curse of dimensionality,Artificial intelligence,Electroencephalography,Machine learning
Conference
Volume
ISSN
Citations 
9094
0302-9743
1
PageRank 
References 
Authors
0.36
14
5
Name
Order
Citations
PageRank
Pedro Martín-Smith110.36
J. Ortega294073.05
Javier Asensio-Cubero3172.84
John Q. Gan4184.87
Andrés Ortiz519525.64