@inproceedings{Schneider2017Visua-41755,
  year={2017},
  title={Visual Integration of Data and Model Space in Ensemble Learning},
  booktitle={Symposium on Visualization in Data Science (VDS) at IEEE VIS 2017},
  author={Schneider, Bruno and Jäckle, Dominik and Stoffel, Florian and Diehl, Alexandra and Fuchs, Johannes and Keim, Daniel A.},
  note={Best paper award}
}

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    <dc:creator>Stoffel, Florian</dc:creator>
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    <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2018-03-13T12:43:30Z</dc:date>
    <dc:contributor>Jäckle, Dominik</dc:contributor>
    <dc:creator>Schneider, Bruno</dc:creator>
    <dc:contributor>Schneider, Bruno</dc:contributor>
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    <dc:contributor>Keim, Daniel A.</dc:contributor>
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    <dc:creator>Jäckle, Dominik</dc:creator>
    <dcterms:issued>2017</dcterms:issued>
    <dcterms:abstract xml:lang="eng">Ensembles of classifier models typically deliver superior performance and can outperform single classifier models given a dataset and classification task at hand. However, the gain in performance comes together with the lack in comprehensibility, posing a challenge to understand how each model affects the classification outputs and where the errors come from. We propose a tight visual integration of the data and the model space for exploring and combining classifier models. We introduce a workflow that builds upon the visual integration and enables the effective exploration of classification outputs and models. We then present a use case in which we start with an ensemble automatically selected by a standard ensemble selection algorithm, and show how we can manipulate models and alternative combinations.</dcterms:abstract>
    <dc:contributor>Stoffel, Florian</dc:contributor>
    <dc:creator>Keim, Daniel A.</dc:creator>
    <dc:contributor>Diehl, Alexandra</dc:contributor>
    <dc:creator>Diehl, Alexandra</dc:creator>
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    <dc:contributor>Fuchs, Johannes</dc:contributor>
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    <dc:creator>Fuchs, Johannes</dc:creator>
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