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Hyperparameter Optimisation for Improving Classification under Class Imbalance

Jiawen Kong; Wojtek Kowalczyk; Duc Anh Nguyen; Stefan Menzel; Thomas Bäck


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  <identifier identifierType="DOI">10.5281/zenodo.3855193</identifier>
  <creators>
    <creator>
      <creatorName>Jiawen Kong</creatorName>
      <affiliation>University of Leiden</affiliation>
    </creator>
    <creator>
      <creatorName>Wojtek Kowalczyk</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-6973-1341</nameIdentifier>
      <affiliation>University of Leiden</affiliation>
    </creator>
    <creator>
      <creatorName>Duc Anh Nguyen</creatorName>
      <affiliation>University of Leiden</affiliation>
    </creator>
    <creator>
      <creatorName>Stefan Menzel</creatorName>
      <affiliation>Honda Research Institute Europe GmBH</affiliation>
    </creator>
    <creator>
      <creatorName>Thomas Bäck</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0001-6768-1478</nameIdentifier>
      <affiliation>University of Leiden</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Hyperparameter Optimisation for Improving Classification under Class Imbalance</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2020</publicationYear>
  <subjects>
    <subject>Class Imbalance</subject>
    <subject>Hyperparameter Optimisation</subject>
    <subject>Overlapping Classes</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2020-02-20</date>
  </dates>
  <language>en</language>
  <resourceType resourceTypeGeneral="Software"/>
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  <rightsList>
    <rights rightsURI="https://opensource.org/licenses/GPL-3.0">GNU General Public License v3.0 or later</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
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  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;This is the source code used in the paper below:&lt;/p&gt;

&lt;p&gt;Jiawen Kong, Wojtek Kowalczyk, Duc Anh Nguyen, Stefan Menzel and Thomas B&amp;auml;ck, &amp;ldquo;Hyperparameter Optimisation for Improving Classification under Class Imbalance&amp;rdquo;, in 2019 IEEE Symposium Series on Computational Intelligence (SSCI), Xiamen, China, 6-9 December 2019, doi:&amp;nbsp;10.1109/SSCI44817.2019.9002679&lt;/p&gt;

&lt;p&gt;Although the class-imbalance classification problem has caught a huge amount&amp;nbsp;&lt;br&gt;
of attention, hyperparameter optimisation has not been studied in detail in&amp;nbsp;&lt;br&gt;
this field. Both classification algorithms and resampling techniques involve&amp;nbsp;&lt;br&gt;
some hyperparameters that can be tuned. This paper sets up several&amp;nbsp;&lt;br&gt;
experiments and draws the conclusion that, compared to using default&amp;nbsp;&lt;br&gt;
hyperparameters, applying hyperparameter optimisation for both&amp;nbsp;&lt;br&gt;
classification algorithms and resampling approaches can produce the best&amp;nbsp;&lt;br&gt;
results for classifying the imbalanced datasets. Moreover, this paper shows&amp;nbsp;&lt;br&gt;
that data complexity, especially the overlap between classes, has a big impact&amp;nbsp;&lt;br&gt;
on the potential improvement that can be achieved through hyperparameter&amp;nbsp;&lt;br&gt;
optimisation. Results of our experiments also indicate that using resampling&amp;nbsp;&lt;br&gt;
techniques cannot improve the performance for some complex datasets, which&amp;nbsp;&lt;br&gt;
further emphasizes the importance of analyzing data complexity before dealing&amp;nbsp;&lt;br&gt;
with imbalanced datasets.&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/H2020/766186/">766186</awardNumber>
      <awardTitle>Experience-based Computation: Learning to Optimise</awardTitle>
    </fundingReference>
  </fundingReferences>
</resource>
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