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Feature Selection with Neural Networks
Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).ORCID iD: 0000-0003-2185-8973
Department of Applied Electronics, Kaunas University of Technology, LT-3031, Kaunas, Lithuania.
2002 (English)In: Pattern Recognition Letters, ISSN 0167-8655, E-ISSN 1872-7344, Vol. 23, no 11, p. 1323-1335Article in journal (Refereed) Published
Abstract [en]

We present a neural network based approach for identifying salient features for classification in feedforward neural networks. Our approach involves neural network training with an augmented cross-entropy error function. The augmented error function forces the neural network to keep low derivatives of the transfer functions of neurons when learning a classification task. Such an approach reduces output sensitivity to the input changes. Feature selection is based on the reaction of the cross-validation data set classification error due to the removal of the individual features. We demonstrate the usefulness of the proposed approach on one artificial and three real-world classification problems. We compared the approach with five other feature selection methods, each of which banks on a different concept. The algorithm developed outperformed the other methods by achieving higher classification accuracy on all the problems tested.

Place, publisher, year, edition, pages
Amsterdam: Elsevier, 2002. Vol. 23, no 11, p. 1323-1335
Keywords [en]
classification, neural network, feature selection, regularization
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:hh:diva-98DOI: 10.1016/S0167-8655(02)00081-8ISI: 000176094200009Scopus ID: 2-s2.0-0036721934OAI: oai:DiVA.org:hh-98DiVA, id: diva2:235620
Available from: 2009-09-17 Created: 2009-09-17 Last updated: 2018-01-13Bibliographically approved

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Verikas, Antanas

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
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  • vancouver
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  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
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  • asciidoc
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