1 Ocak 2014 Dergi makalesi Açık Erişim

Yildiz, Olcay Taner

Dublin Core

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  <dc:creator>Yildiz, Olcay Taner</dc:creator>
  <dc:date>2014-01-01</dc:date>
  <dc:description>In many pattern recognition applications, feature space expansion is a key step for improving the performance of the classifier. In this paper, we (i) expand the discrete feature space by generating all orderings of values of k discrete attributes exhaustively, (ii) modify the well-known decision tree and rule induction classifiers (ID3, Quilan, 1986 [1] and Ripper, Cohen, 1995 [2]) using these orderings as the new attributes. Our simulation results on 15 datasets from UCI repository [3] show that the novel classifiers perform better than the proper ones in terms of error rate and complexity. (C) 2013 Elsevier Ltd. All rights reserved.</dc:description>
  <dc:identifier>https://aperta.ulakbim.gov.trrecord/63613</dc:identifier>
  <dc:identifier>oai:zenodo.org:63613</dc:identifier>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>http://www.opendefinition.org/licenses/cc-by</dc:rights>
  <dc:source>PATTERN RECOGNITION 47(5) 1988-1993</dc:source>
  <dc:title>On the feature extraction in discrete space</dc:title>
  <dc:type>info:eu-repo/semantics/article</dc:type>
  <dc:type>publication-article</dc:type>
</oai_dc:dc>