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%A B. Auffarth %A M. Lopez %A J. Cerquides %J Advances in data mining: medical applications, e-commerce, marketing, and theoretical aspects. LNAI 5077 %T Hopfield Networks in Relevance and Redundancy Feature Selection Applied to Classification of Biomedical High-Resolution Micro-CT Images %X 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. %K feature selection, image features, pattern classification %P 16-31 %E Petra Perner %D 2008 %I Springer Heidelberg %L cogprints7061