Genetic Algorithm for Feature Subset Selection with Exploitation of Feature Correlations from Continuous Wavelet Transform: a re

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. Keywords--Classification, genetic algorithm, hierarchical agglomerative clustering, wavelet transform.

• Type: Conference
• Year: 2004
• Where: IJIT
• Authors: Gert Van Dijck, Marc M. Van Hulle, M. Wevers
• Keywords: Artificial Intelligence, Correlation Structure, Genetic Algorithm, IJIT 2004, Subset Selection Algorithm