@misc{cogprints7061,
           month = {July},
          author = {B. Auffarth and M. Lopez and J. Cerquides},
          editor = {Petra Perner},
           title = {Hopfield Networks in Relevance and Redundancy Feature Selection Applied to Classification of Biomedical High-Resolution Micro-CT Images},
       publisher = {Springer Heidelberg},
         journal = {Advances in data mining: medical applications, e-commerce, marketing, and theoretical aspects. LNAI 5077},
           pages = {16--31},
            year = {2008},
        keywords = {feature selection, image features, pattern classification},
             url = {http://cogprints.org/7061/},
        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. }
}

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