Hopﬁeld Networks in Relevance and Redundancy Feature Selection Applied to Classiﬁcation of Biomedical High-Resolution Micro-CT Images
2008 (English)In: Advances in Data Mining: Medical Applications, E-Commerce, Marketing, and Theoretical Aspects / [ed] Petra Perner, Heidelberg: Springer , 2008, 16-31 p.Chapter in book (Refereed)
We study filter-based feature selection methods for classification of biomedical images. For feature selection, we use two filters - a relevance filter which measures usefulness of individual features for target prediction, and a redundancy filter, which measures similarity between features. As selection method that combines relevance and redundancy we try out a Hopfield network. We experimentally compare selection methods, running unitary redundancy and relevance filters, against a greedy algorithm with redundancy thresholds , the min-redundancy max-relevance integration [8,23,36], and our Hopfield network selection. We conclude that on the whole, Hopfield selection was one of the most successful methods, outperforming min-redundancy max-relevance when more features are selected.
Place, publisher, year, edition, pages
Heidelberg: Springer , 2008. 16-31 p.
, Lecture notes in artificial intelligence, ISSN 3540707174 ; 5077
feature selection, image features, pattern classification
Engineering and Technology
Research subject SRA - ICT
IdentifiersURN: urn:nbn:se:kth:diva-48062DOI: 10.1007/978-3-540-70720-2_2ISI: 000258494700002OAI: oai:DiVA.org:kth-48062DiVA: diva2:456736
8th Industrial Conference on Data Mining. Leipzig, GERMANY. JUL 16-18, 2008
QC 201111162011-11-152011-11-152016-08-09Bibliographically approved