• DocumentCode
    2331941
  • Title

    Visualizing decision table classifiers

  • Author

    Becker, Barry G.

  • Author_Institution
    Silicon Graphics Inc., Mountain View, CA, USA
  • fYear
    1998
  • fDate
    19-20 Oct 1998
  • Firstpage
    102
  • Abstract
    Decision tables, like decision trees or neural nets, are classification models used for prediction. They are induced by machine learning algorithms. A decision table consists of a hierarchical table in which each entry in a higher level table gets broken down by the values of a pair of additional attributes to form another table. The structure is similar to dimensional stacking. A visualization method is presented that allows a model based on many attributes to be understood even by those unfamiliar with machine learning. Various forms of interaction are used to make this visualization more useful than other static designs
  • Keywords
    data visualisation; decision tables; learning (artificial intelligence); pattern classification; attributes; classification models; decision table classifier visualization; dimensional stacking; hierarchical table; interaction; machine learning algorithms; prediction; Classification tree analysis; Data mining; Data visualization; Decision trees; Displays; Graphics; Layout; Predictive models; Stacking; Training data;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Information Visualization, 1998. Proceedings. IEEE Symposium on
  • Conference_Location
    Research Triangle, CA
  • Print_ISBN
    0-8186-9093-3
  • Type

    conf

  • DOI
    10.1109/INFVIS.1998.729565
  • Filename
    729565