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.
This research was supported by the Spanish MEC Project “3D Reconstruction, classification and visualization of temporal sequences of bioimplant Micro-CT images“ (MAT-2005-07244-C03-03).
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Auffarth, B., López, M., Cerquides, J. (2008). Hopfield Networks in Relevance and Redundancy Feature Selection Applied to Classification of Biomedical High-Resolution Micro-CT Images. In: Perner, P. (eds) Advances in Data Mining. Medical Applications, E-Commerce, Marketing, and Theoretical Aspects. ICDM 2008. Lecture Notes in Computer Science(), vol 5077. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-70720-2_2
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