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Towards the optimal feature selection in high-dimensional bayesian network classifiers
Responsible organisation
2004 (English)Report (Other academic)
Abstract [en]

Incorporating subset selection into a classification method often carries a number of advantages, especially when operating in the domain of high-dimensional features. In this paper, we focus on Bayesian network (BN) classifiers and formalize the feature selection from a perspective of improving classification accuracy. To exploring the effect of high-dimensionality we apply the

growing dimension asymptotics, meaning that the number of training examples is relatively small compared to the number of feature nodes. In order to ascertain which set of features is indeed relevant for a classification task, we introduce a distance-based scoring measure

reflecting how well the set separates different classes. This score is then employed to feature selection, using the weighted form of BN classifier. The idea is to view weights as inclusion-exclusion factors which eliminates the sets of features whose separation score do not exceed a given threshold. We establish the asymptotic optimal threshold and demonstrate that the proposed selection technique carries improvements over classification accuracy for different a priori assumptions concerning the separation strength.

Place, publisher, year, edition, pages
UmeƄ: SLU, Centre of Biostochastics , 2004. , p. 14
Series
Research report / Centre of Biostochastics,, ISSN 1651-8543 ; 2004 : 1
Keywords [en]
Bayesian network, augmenting, separation strength, growing dimension
National Category
Mathematics
Identifiers
URN: urn:nbn:se:miun:diva-5653Local ID: 1677OAI: oai:DiVA.org:miun-5653DiVA, id: diva2:30686
Available from: 2008-09-30 Created: 2008-09-30 Last updated: 2011-04-12Bibliographically approved

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fulltext(211 kB)97 downloads
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Authority records BETA

Pavlenko, TatjanaHall, Mikael

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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • 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