Title: Speeding Up Feature Subset Selection through Mutual Information Relevance Filtering
Authors: Van Dijck, Gert
Van Hulle, Marc #
Issue Date: Sep-2007
Publisher: Springer
Host Document: Lecture Notes in Computer Science vol:4702
Conference: 18th European Conference on Machine Learning (ECML 2007)/11th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD 2007) location:Warsaw, Poland date:17-21 September 2007
Abstract: A relevance filter is proposed which removes features based on the mutual information between class labels and features. It is proven that both feature independence and class conditional feature independence are required for the filter to be statistically optimal. This could be shown by establishing a relationship with the conditional relative entropy framework for feature selection. Removing features at various significance levels as a preprocessing step to sequential forward search leads to a huge increase in speed, without a decrease in classification accuracy. These results are shown based on experiments with 5 high-dimensional publicly available gene expression data sets.
ISBN: 978-3-540-74975-2
ISSN: 0302-9743
Publication status: published
KU Leuven publication type: IC
Appears in Collections:Laboratory for Neuro- and Psychofysiology
Research Group Neurophysiology
# (joint) last author

Files in This Item:
File Description Status SizeFormat
MI_ECML_final_29_06_paper_63.pdfMain article Published 257KbAdobe PDFView/Open Request a copy

These files are only available to some KU Leuven Association staff members


All items in Lirias are protected by copyright, with all rights reserved.

© Web of science