"7061","Hopfield Networks in Relevance and Redundancy Feature Selection Applied to Classification of Biomedical High-Resolution Micro-CT Images","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. ","http://cogprints.org/7061/","Auffarth, B. and Lopez, M. and Cerquides, J.","Perner, Petra","       <span class=\"person_name\">Auffarth, B.</span> and <span class=\"person_name\">Lopez, M.</span> and <span class=\"person_name\">Cerquides, J.</span>     (2008)  <a href=\"http://cogprints.org/7061/\"><em>Hopfield Networks in Relevance and Redundancy Feature Selection Applied to Classification of Biomedical High-Resolution Micro-CT Images.</em></a>       [Book Chapter]           ","","2008-07-17"