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Selecting salient features for classification based on neural network committees
Department of Applied Electronics, Kaunas University of Technology LT-3031, Kaunas, Lithuania.
Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).ORCID iD: 0000-0003-2185-8973
2004 (English)In: Pattern Recognition Letters, ISSN 0167-8655, E-ISSN 1872-7344, Vol. 25, no 16, p. 1879-1891Article in journal (Refereed) Published
Abstract [en]

Aggregating outputs of multiple classifiers into a committee decision is one of the most important techniques for improving classification accuracy. The issue of selecting an optimal subset of relevant features plays also an important role in successful design of a pattern recognition system. In this paper, we present a neural network based approach for identifying salient features for classification in neural network committees. Feature selection is based on two criteria, namely the reaction of the cross-validation data set classification error due to the removal of the individual features and the diversity of neural networks comprising the committee. The algorithm developed removed a large number of features from the original data sets without reducing the classification accuracy of the committees. The accuracy of the committees utilizing the reduced feature sets was higher than those exploiting all the original features.

Place, publisher, year, edition, pages
Amsterdam: Elsevier Science , 2004. Vol. 25, no 16, p. 1879-1891
Keywords [en]
Classification, Decision fusion, Feature selection, Neural network committee
National Category
Mechanical Engineering Basic Medicine
Identifiers
URN: urn:nbn:se:hh:diva-241DOI: 10.1016/j.patrec.2004.08.018ISI: 000225199400010Scopus ID: 2-s2.0-8344257309Local ID: 2082/536OAI: oai:DiVA.org:hh-241DiVA, id: diva2:237419
Available from: 2006-11-24 Created: 2006-11-24 Last updated: 2022-09-13Bibliographically approved

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Verikas, Antanas

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CiteExportLink to record
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Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
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Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf