---
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\r\nmore features are selected. "
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creators_name:
  - family: Auffarth
    given: B.
    honourific: ''
    lineage: ''
  - family: Lopez
    given: M.
    honourific: ''
    lineage: ''
  - family: Cerquides
    given: J.
    honourific: ''
    lineage: ''
date: 2008-07-17
date_type: published
datestamp: 2010-10-18 11:03:10
department: ~
dir: disk0/00/00/70/61
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editors_id: []
editors_name:
  - family: Perner
    given: Petra
    honourific: ''
    lineage: ''
eprint_status: archive
eprintid: 7061
fileinfo: /style/images/fileicons/application_pdf.png;/7061/1/leipzip08.pdf
full_text_status: public
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keywords: 'feature selection, image features, pattern classification'
lastmod: 2011-03-11 08:57:45
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metadata_visibility: show
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pagerange: 16-31
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publication: 'Advances in data mining: medical applications, e-commerce, marketing, and theoretical aspects. LNAI 5077'
publisher: Springer Heidelberg
refereed: TRUE
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relation_type: []
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reportno: ~
rev_number: 28
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status_changed: 2010-10-18 11:03:10
subjects:
  - comp-sci-mach-learn
  - comp-sci-stat-model
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title: Hopfield Networks in Relevance and Redundancy Feature Selection Applied to Classification of Biomedical High-Resolution Micro-CT Images
type: bookchapter
userid: 11278
volume: ~
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