Title: Genetic Algorithm for Feature Subset Selection with Exploitation of Feature Correlations from the Continuous Wavelet Transform: a real-case Application
Authors: Van Dijck, Gert ×
Van Hulle, Marc
Wevers, Martine #
Issue Date: 2005
Host Document: International Journal of Computational Intelligence vol:1 issue:4 pages:233-237
Conference: International Conference on Computational Intelligence location:Istanbul, Turkey date:17-19 December
Abstract: A genetic algorithm (GA) based feature subset selection algorithm is proposed in which the correlation structure of the features is exploited. The subset of features is validated according to the classification performance. Features derived from the continuous wavelet transform are potentially strongly correlated. GA’s that do not take the correlation structure of features into account are inefficient. The proposed algorithm forms clusters of correlated features and searches for a good candidate set of clusters. Secondly a search within the clusters is performed. Different simulations of the algorithm on a real-case data set with strong correlations between features show the increased classification performance. Comparison is performed with a standard GA without use of the correlation structure.
ISBN: 975-98458
ISSN: 1304-4508
Publication status: published
KU Leuven publication type: IC
Appears in Collections:Laboratory for Neuro- and Psychofysiology
Research Group Neurophysiology
Mechanical Metallurgy Section (-)
× corresponding author
# (joint) last author

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