Comparison of Redundancy and Relevance Measures for Feature Selection in Tissue Classification of CT images
2010 (English)In: Advances in Data Mining: Applications in Medicine, Web Mining, Marketing, Image and Signal Mining / [ed] Petra Perner, Heidelberg: Springer Berlin/Heidelberg, 2010, 248-262 p.Chapter in book (Refereed)
In this paper we report on a study on feature selection within the minimum-redundancy maximum-relevance framework. Features are ranked by their correlations to the target vector. These relevance scores are then integrated with correlations between features in order to ob- tain a set of relevant and least-redundant features. Applied measures of correlation or distributional similarity for redundancy and relevance include Kolmogorov-Smirnov (KS) test, Spearman correlations, Jensen-Shannon divergence, and the sign-test. We introduce a metric called “value difference metric“ (VDM) and present a simple measure, which we call “fit criterion“ (FC). We draw conclusions about the usefulness of different measures. While KS-test and sign-test provided useful information, Spearman correlations are not fit for comparison of data of different measurement intervals. VDM was very good in our experiments as both redundancy and relevance measure. Jensen-Shannon and the sign-test are good redundancy measure alternatives and FC is a good relevance measure alternative.
Place, publisher, year, edition, pages
Heidelberg: Springer Berlin/Heidelberg, 2010. 248-262 p.
, Lecture Notes in Artificial Intelligence, ISSN 0302-9743 ; 6171
distributional similarity; divergence measure; feature selection; relevance and redundancy
Research subject SRA - ICT
IdentifiersURN: urn:nbn:se:kth:diva-48064DOI: 10.1007/978-3-642-14400-4_20ISI: 000286902300020OAI: oai:DiVA.org:kth-48064DiVA: diva2:456741
10th Industrial Conference on Data Mining. Berlin, GERMANY. JUL 12-14, 2010
QC 201111162011-11-152011-11-152016-08-09Bibliographically approved