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Scoring Feature Subsets for Separation power in Supervised Bayes Classification
Mid Sweden University, Faculty of Science, Technology and Media, Department of Engineering, Physics and Mathematics.
Mid Sweden University, Faculty of Science, Technology and Media, Department of Engineering, Physics and Mathematics.
2007 (English)In: Advances in Intelligent and Soft Computing, ISSN 1867-5662, E-ISSN 1867-5670, Vol. 37, 383-391 p.Article in journal (Refereed) Published
Abstract [en]

We present a method for evaluating the discriminative power of compact feature combinations (blocks) using the distance-based scoring measure, yielding an algorithm for selecting feature blocks that significantly contribute to the outcome variation. To estimate classification performance with subset selection in a high dimensional framework we jointly evaluate both stages of the process: selection of significantly relevant blocks and classification. Classification power and performance properties of the classifier with the proposed subset selection technique has been studied on several simulation models and confirms the benefit of this approach.

Place, publisher, year, edition, pages
Berlin: Springer , 2007. Vol. 37, 383-391 p.
Keyword [en]
multivariate statistics, classification
National Category
Mathematics
Identifiers
URN: urn:nbn:se:miun:diva-3867DOI: 10.1007/3-540-34777-1_45Scopus ID: 2-s2.0-58149242746Local ID: 4162ISBN: 978-3-540-34776-7 (print)OAI: oai:DiVA.org:miun-3867DiVA: diva2:28899
Available from: 2008-09-30 Created: 2008-09-30 Last updated: 2016-09-26Bibliographically approved

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Pavlenko, TatjanaFridén, Håkan
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