• DocumentCode
    1386623
  • Title

    Constructing Bayesian networks to predict uncollectible telecommunications accounts

  • Author

    Ezawa, Kazuo J. ; Norton, Steven W.

  • Author_Institution
    AT&T Bell Labs., Murray Hill, NJ, USA
  • Volume
    11
  • Issue
    5
  • fYear
    1996
  • fDate
    10/1/1996 12:00:00 AM
  • Firstpage
    45
  • Lastpage
    51
  • Abstract
    The complexities of building models that can predict whether a customer account or transaction is collectible are greater than most current learning systems can handle. The authors describe software that builds Bayesian network models for such predictions. They also examine how varying model parameters and hence model structure can affect predictive accuracy
  • Keywords
    Bayes methods; learning systems; neural nets; prediction theory; Bayesian networks; customer account; model parameters; model structure; predictions; predictive accuracy; uncollectible telecommunications accounts; Bayesian methods; Buildings; Communication industry; Laboratories; Learning systems; Predictive models; Profitability; Risk management; Telecommunications; Transaction databases;
  • fLanguage
    English
  • Journal_Title
    IEEE Expert
  • Publisher
    ieee
  • ISSN
    0885-9000
  • Type

    jour

  • DOI
    10.1109/64.539016
  • Filename
    539016
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1386623