1 Ocak 2014 Dergi makalesi Açık Erişim

Yildiz, Olcay Taner

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  <identifier identifierType="URL">https://aperta.ulakbim.gov.tr/record/63613</identifier>
  <creators>
    <creator>
      <creatorName>Yildiz, Olcay Taner</creatorName>
      <givenName>Olcay Taner</givenName>
      <familyName>Yildiz</familyName>
    </creator>
  </creators>
  <titles>
    <title>On The Feature Extraction In Discrete Space</title>
  </titles>
  <publisher>Aperta</publisher>
  <publicationYear>2014</publicationYear>
  <dates>
    <date dateType="Issued">2014-01-01</date>
  </dates>
  <resourceType resourceTypeGeneral="Text">Journal article</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://aperta.ulakbim.gov.tr/record/63613</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.1016/j.patcog.2013.11.023</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="http://www.opendefinition.org/licenses/cc-by">Creative Commons Attribution</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
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  <descriptions>
    <description descriptionType="Abstract">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.</description>
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