Classifier Hypothesis Generation Using Visual Analysis Methods

Christin Seifert, Vedran Sabol, Michael Granitzer

    Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

    6 Citations (Scopus)


    Classifiers can be used to automatically dispatch the abundance of\nnewly created documents to recipients interested in particular topics.\nIdentification of adequate training examples is essential for classification\nperformance, but it may prove to be a challenging task in large document\nrepositories. We propose a classifier hypothesis generation method\nrelying on automated analysis and information visualisation. In our\napproach visualisations are used to explore the document sets and\nto inspect the results of machine learning methods, allowing the\nuser to assess the classifier performance and adapt the classifier\nby gradually refining the training set.
    Original languageEnglish
    Title of host publicationNetworked Digital Technologies
    Subtitle of host publicationSecond International Conference, NDT 2010, Prague, Czech Republic, July 7-9, 2010. Proceedings
    EditorsFilip Zavoral, Jakub Yaghob, Pit Pichappan, Eyas El-Qawasmeh
    Number of pages14
    ISBN (Electronic)978-3-642-14292-5
    ISBN (Print)978-3-642-14291-8
    Publication statusPublished - 2010
    Event2nd International Conference on Networked Digital Technologies, NDT 2010 - Prague, Czech Republic
    Duration: 7 Jul 20109 Jul 2010
    Conference number: 2

    Publication series

    NameCommunications in Computer and Information Science
    ISSN (Print)1865-0929
    ISSN (Electronic)1865-0937


    Conference2nd International Conference on Networked Digital Technologies, NDT 2010
    Abbreviated titleNDT
    CountryCzech Republic


    • Text Categorisation
    • Visual Analysis

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