Integrating Data and Model Space in Ensemble Learning by Visual Analytics

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2021
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IEEE Transactions on Big Data. Institute of Electrical and Electronics Engineers (IEEE). 2021, 7(3), pp. 483-496. ISSN 2372-2096. eISSN 2332-7790. Available under: doi: 10.1109/TBDATA.2018.2877350
Zusammenfassung

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 of comprehensibility, posing a challenge to understand how each model affects the classification outputs and from where the errors come. We propose a tight visual integration of the data and the model space for exploring and combining classifier models. We introduce an interactive workflow that builds upon the visual integration and enables the effective exploration of classification outputs and models. The involvement of the user is key to our approach. Therefore, we elaborate on the role of the human and connect our approach to theoretical frameworks on human-centered machine learning. We showcase the usefulness of our approach and the integration of the user via binary and multiclass classification problems. Based on ensembles automatically selected by a standard ensemble selection algorithm, the user can manipulate models and alternative combinations.

Zusammenfassung in einer weiteren Sprache
Fachgebiet (DDC)
004 Informatik
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Classification, Ensemble learning, Data visualization, Graphical user interfaces
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Zitieren
ISO 690SCHNEIDER, Bruno, Dominik JÄCKLE, Florian STOFFEL, Alexandra DIEHL, Johannes FUCHS, Daniel A. KEIM, 2021. Integrating Data and Model Space in Ensemble Learning by Visual Analytics. In: IEEE Transactions on Big Data. Institute of Electrical and Electronics Engineers (IEEE). 2021, 7(3), pp. 483-496. ISSN 2372-2096. eISSN 2332-7790. Available under: doi: 10.1109/TBDATA.2018.2877350
BibTex
@article{Schneider2021Integ-44388,
  year={2021},
  doi={10.1109/TBDATA.2018.2877350},
  title={Integrating Data and Model Space in Ensemble Learning by Visual Analytics},
  number={3},
  volume={7},
  issn={2372-2096},
  journal={IEEE Transactions on Big Data},
  pages={483--496},
  author={Schneider, Bruno and Jäckle, Dominik and Stoffel, Florian and Diehl, Alexandra and Fuchs, Johannes and Keim, Daniel A.}
}
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