Journal article Open Access

Informative trees by visual pruning

Iorio, Carmela; Aria, Massimo; D'Ambrosio, Antonio; Siciliano, Roberta


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  <identifier identifierType="URL">https://zenodo.org/record/3267338</identifier>
  <creators>
    <creator>
      <creatorName>Iorio, Carmela</creatorName>
      <givenName>Carmela</givenName>
      <familyName>Iorio</familyName>
      <affiliation>University of Naples Federico II, Italy</affiliation>
    </creator>
    <creator>
      <creatorName>Aria, Massimo</creatorName>
      <givenName>Massimo</givenName>
      <familyName>Aria</familyName>
      <affiliation>University of Naples Federico II, Italy</affiliation>
    </creator>
    <creator>
      <creatorName>D'Ambrosio, Antonio</creatorName>
      <givenName>Antonio</givenName>
      <familyName>D'Ambrosio</familyName>
      <affiliation>University of Naples Federico II, Italy</affiliation>
    </creator>
    <creator>
      <creatorName>Siciliano, Roberta</creatorName>
      <givenName>Roberta</givenName>
      <familyName>Siciliano</familyName>
      <affiliation>University of Naples Federico II, Italy</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Informative trees by visual pruning</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2019</publicationYear>
  <subjects>
    <subject>CART</subject>
    <subject>Impurity proportional reduction</subject>
    <subject>Cost-complexity pruning</subject>
    <subject>Visualization</subject>
    <subject>Supervised statistical learning</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2019-08-01</date>
  </dates>
  <language>en</language>
  <resourceType resourceTypeGeneral="JournalArticle"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/3267338</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.1016/j.eswa.2019.03.018</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://zenodo.org/communities/magic</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;The aim of this study is to provide visual pruning and decision tree selection for classification and regression trees. Specifically, we introduce an unedited tree graph to be made informative for recursive tree data partitioning. A decision tree is visually selected through a dendrogram-like procedure or through automatic tree-size selection. Our proposal is a one-step procedure whereby the most predictive paths are visualized. This method appears to be useful in all real world cases where tree-path interpretation is crucial. Experimental evaluations using real world data sets are presented. The performance was very similar to Classification and Regression Trees (CART) benchmarking methodology, showing that our method is a valid alternative to the well-known method of cost-complexity pruning.&lt;/p&gt;</description>
  </descriptions>
  <fundingReferences>
    <fundingReference>
      <funderName>European Commission</funderName>
      <funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/501100000780</funderIdentifier>
      <awardNumber awardURI="info:eu-repo/grantAgreement/EC/H2020/689669/">689669</awardNumber>
      <awardTitle>Moving Towards Adaptive Governance in Complexity: Informing Nexus Security</awardTitle>
    </fundingReference>
  </fundingReferences>
</resource>
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