Gert Van Dijck
Katholieke Universiteit Leuven
23 Papers
88 Citations
Gert Van Dijck is an academic researcher from Katholieke Universiteit Leuven. The author has contributed to research in topics: Mutual information & Wavelet packet decomposition. The author has an hindex of 9, co-authored 23 publications.
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Papers
•Journal Article
Speeding Up the Wrapper Feature Subset Selection in Regression by Mutual Information Relevance and Redundancy Analysis
Gert Van Dijck,Marc M. Van Hulle +1 more
TL;DR: A hybrid filter/wrapper feature subset selection algorithm for regression using mutual information between regression and target variables and introducing permutation tests to find statistically significant relevant and redundant features.
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•Proceedings Article
Genetic Algorithm for Feature Subset Selection with Exploitation of Feature Correlations from Continuous Wavelet Transform: a real-case Application
Gert Van Dijck,Marc M. Van Hulle,Martine Wevers +2 more
- 01 Jan 2004
TL;DR: A genetic algorithm (GA) based feature subset selection algorithm is proposed in which correlation structure of the features is exploited and the subset of features is validated according to the classification performance.
17
Posterior probability profiles for the automated assessment of the recovery of patients with stroke from activity of daily living tasks
TL;DR: The assessment of recovery after stroke can be automated by means of posterior probability profiles due to their high correlation with the Fugl-Meyer assessment, which confirms the importance of a recovery within the first weeks after stroke to obtain a higher recovery plateau compared to later changes in recovery.
14
Wavelet packet decomposition for the identification of corrosion type from acoustic emission signals
TL;DR: The detection of corrosion and the prediction of the type of corrosion are studied by means of the acoustic emission technique to prove that the pair-wise Kullback–Leibler divergence used in the local discriminant basis algorithm requires class conditional independence of the wavelet coefficients.
13
Information Theory Filters for Wavelet Packet Coefficient Selection with Application to Corrosion Type Identification from Acoustic Emission Signals
Gert Van Dijck,Marc M. Van Hulle +1 more
TL;DR: Wavelet packet decomposition is applied to extract features from acoustic emission signals to distinguish between the most important types of corrosion processes in the chemical process industry: uniform corrosion, pitting and stress corrosion cracking.
11