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Fusion of Dimensionality Reduction Methods: a Case Study in Microarray Classification
Dept. of Computer and Systems Sciences, Stockholm University, Sweden.
University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre.
2009 (English)In: Proceedings of the 12th International Conference on Information Fusion, ISIF , 2009, 460-465 p.Conference paper, (Refereed)
Abstract [en]

Dimensionality reduction has been demonstrated to improve the performance of the k-nearest neighbor (kNN) classifier for high-dimensional data sets, such as microarrays. However, the effectiveness of different dimensionality reduction methods varies, and it has been shown that no single method constantly outperforms the others. In contrast to using a single method, two approaches to fusing the result of applying dimensionality reduction methods are investigated: feature fusion and classifier fusion. It is shown that by fusing the output of multiple dimensionality reduction techniques, either by fusing the reduced features or by fusing the output of the resulting classifiers, both higher accuracy and higher robustness towards the choice of number of dimensions is obtained.

Place, publisher, year, edition, pages
ISIF , 2009. 460-465 p.
Keyword [en]
Nearest neighbor classification, dimensionality reduction, feature fusion, classifier fusion, microarrays
National Category
Computer and Information Science
Research subject
Technology
Identifiers
URN: urn:nbn:se:his:diva-3455ISI: 000273560000060Scopus ID: 2-s2.0-70449375084ISBN: 978-0-9824-4380-4 (print)OAI: oai:DiVA.org:his-3455DiVA: diva2:273184
Conference
Fusion 2009 : the 12th International Conference on Information Fusion : Grand Hyatt Seattle, Seattle, Washington, USA, 6-9 July, 2009
Available from: 2009-10-20 Created: 2009-10-20 Last updated: 2017-06-08Bibliographically approved

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Scopushttp://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-33439

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CiteExportLink to record
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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
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  • asciidoc
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