Hopfield Networks in Relevance and Redundancy Feature Selection Applied to Classification of Biomedical High-Resolution Micro-CT Images

Authors:
Benjamin Auffarth;Maite López;Jesús Cerquides
Affiliations:
Volume Visualization and Artificial Intelligence research group, Departament de Matemàtica Aplicada i Anàlisi (MAIA), Universitat de Barcelona, Barcelona, Spain 08007;Volume Visualization and Artificial Intelligence research group, Departament de Matemàtica Aplicada i Anàlisi (MAIA), Universitat de Barcelona, Barcelona, Spain 08007;Volume Visualization and Artificial Intelligence research group, Departament de Matemàtica Aplicada i Anàlisi (MAIA), Universitat de Barcelona, Barcelona, Spain 08007
Venue:
ICDM '08 Proceedings of the 8th industrial conference on Advances in Data Mining: Medical Applications, E-Commerce, Marketing, and Theoretical Aspects
Year:
2008

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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.