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TY - GEN ID - cogprints7061 UR - http://cogprints.org/7061/ A1 - Auffarth, B. A1 - Lopez, M. A1 - Cerquides, J. Y1 - 2008/07/17/ N2 - 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. PB - Springer Heidelberg KW - feature selection KW - image features KW - pattern classification TI - Hopfield Networks in Relevance and Redundancy Feature Selection Applied to Classification of Biomedical High-Resolution Micro-CT Images SP - 16 AV - public EP - 31 ER -