User-based active learning

Christin Seifert, Michael Granitzer

    Research output: Chapter in Book/Report/Conference proceedingChapterAcademicpeer-review

    21 Citations (Scopus)


    Active learning has been proven a reliable strategy to reduce manual efforts in training data labeling. Such strategies incorporate the user as oracle: the classifier selects the most appropriate example and the user provides the label. While this approach is tailored towards the classifier, more intelligent input from the user may be beneficial. For instance, given only one example at a time users are hardly able to determine whether this example is an outlier or not. In this paper we propose user-based visually-supported active learning strategies that allow the user to do both, selecting and labeling examples given a trained classifier. While labeling is straightforward, selection takes place using a interactive visualization of the classifier's a-posteriori output probabilities. By simulating different user selection strategies we show, that user-based active learning outperforms uncertainty based sampling methods and yields a more robust approach on different data sets. The obtained results point towards the potential of combining active learning strategies with results from the field of information visualization.
    Original languageEnglish
    Title of host publicationProceedings - IEEE International Conference on Data Mining, ICDM
    Place of PublicationPiscataway, NJ
    Number of pages8
    ISBN (Electronic)978-0-7695-4257-7
    ISBN (Print)978-1-4244-9244-2
    Publication statusPublished - 2010

    Publication series

    NameProceedings IEEE International Conference on Data Mining (ICDM)
    ISSN (Print)2375-9232
    ISSN (Electronic)2375-9259


    • Active learning
    • Information visualization
    • User behavior
    • Visualization

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  • Cite this

    Seifert, C., & Granitzer, M. (2010). User-based active learning. In Proceedings - IEEE International Conference on Data Mining, ICDM (pp. 418-425). (Proceedings IEEE International Conference on Data Mining (ICDM); Vol. 2010). Piscataway, NJ: IEEE.