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Evolving Committees of Support Vector Machines
Kaunas University of Technology, Department of Applied Electronics, 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
Kaunas University of Technology, Department of Electrical and Control Equipment, Kaunas, Lithuania.
Kaunas University of Technology, Department of Electrical and Control Equipment, Kaunas, Lithuania.
2007 (English)In: Machine Learning and Data Mining in Pattern Recognition, Proceedings / [ed] Perner, P, Berlin: Springer Berlin/Heidelberg, 2007, p. 263-275Conference paper, Published paper (Refereed)
Abstract [en]

The main emphasis of the technique developed in this work for evolving committees of support vector machines (SVM) is on a two phase procedure to select salient features. In the first phase, clearly redundant features are eliminated based on the paired t-test comparing the SVM output sensitivity-based saliency of the candidate and the noise feature. In the second phase, the genetic search integrating the steps of training, aggregation of committee members, and hyper-parameter as well as feature selection into the same learning process is employed. A small number of genetic iterations needed to find a solution is the characteristic feature of the genetic search procedure developed. The experimental tests performed on five real world problems have shown that significant improvements in correct classification rate can be obtained in a small number of iterations if compared to the case of using all the features available.

Place, publisher, year, edition, pages
Berlin: Springer Berlin/Heidelberg, 2007. p. 263-275
Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; Volume 4571/2007
Keywords [en]
Support vector machines, SVM
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:hh:diva-2062DOI: 10.1007/978-3-540-73499-4_20ISI: 000248523200019Scopus ID: 2-s2.0-37249081487Local ID: 2082/2457ISBN: 978-3-540-73498-7 OAI: oai:DiVA.org:hh-2062DiVA, id: diva2:239280
Conference
5th International Conference on Machine Learning and Data Mining in Pattern Recognition, Leipzig, Germany, July 18-20, 2007
Available from: 2008-10-18 Created: 2008-10-18 Last updated: 2014-11-10Bibliographically approved

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Verikas, Antanas

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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
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