creators_name: Auffarth, B. creators_name: Lopez, M. creators_name: Cerquides, J. editors_name: Perner, Petra type: bookchapter datestamp: 2010-10-18 11:03:10 lastmod: 2011-03-11 08:57:45 metadata_visibility: show title: Hopfield Networks in Relevance and Redundancy Feature Selection Applied to Classification of Biomedical High-Resolution Micro-CT Images ispublished: pub subjects: comp-sci-mach-learn subjects: comp-sci-stat-model full_text_status: public keywords: feature selection, image features, pattern classification abstract: 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 [9], 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. date: 2008-07-17 date_type: published publication: Advances in data mining: medical applications, e-commerce, marketing, and theoretical aspects. LNAI 5077 publisher: Springer Heidelberg pagerange: 16-31 refereed: TRUE citation: Auffarth, B. and Lopez, M. and Cerquides, J. (2008) Hopfield Networks in Relevance and Redundancy Feature Selection Applied to Classification of Biomedical High-Resolution Micro-CT Images. [Book Chapter] document_url: http://cogprints.org/7061/1/leipzip08.pdf