Title: Joint Markov Blankets in Feature Sets Extracted from Wavelet Packet Decompositions
Authors: Van Dijck, Gert ×
Van Hulle, Marc #
Issue Date: Jul-2011
Publisher: Molecular Diversity Preservation International
Series Title: Entropy vol:13 issue:7 pages:1403-1424
Abstract: Since two decades, wavelet packet decompositions have been shown effective as a generic approach to feature extraction from time series and images for the prediction of a target variable. Redundancies exist between the wavelet coefficients and between the energy features that are derived from the wavelet coefficients. We assess these redundancies in wavelet packet decompositions by means of the Markov blanket filtering theory. We introduce the concept of joint Markov blankets. It is shown that joint Markov blankets are a natural extension of Markov blankets, which are defined for single features, to a set of features. We show that these joint Markov blankets exist in feature sets consisting of the wavelet coefficients. Furthermore, we prove that wavelet energy features from the highest frequency resolution level form a joint Markov blanket for all other wavelet energy features. The joint Markov blanket theory indicates that one can expect an increase of classification accuracy with the increase of the frequency resolution level of the energy features.
ISSN: 1099-4300
Publication status: published
KU Leuven publication type: IT
Appears in Collections:Laboratory for Neuro- and Psychofysiology
Research Group Neurophysiology
× corresponding author
# (joint) last author

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