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

Auffarth, B. and Lopez, M. and Cerquides, J. (2008) Hopfield Networks in Relevance and Redundancy Feature Selection Applied to Classification of Biomedical High-Resolution Micro-CT Images. [Book Chapter]

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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.

Item Type:Book Chapter
Keywords:feature selection, image features, pattern classification
Subjects:Computer Science > Machine Learning
Computer Science > Statistical Models
ID Code:7061
Deposited By:Auffarth, Benjamin
Deposited On:18 Oct 2010 12:03
Last Modified:11 Mar 2011 08:57

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