• DocumentCode
    2627905
  • Title

    Lazy MetaCost Naive Bayes

  • Author

    Kotsiantis, Sotiris ; Kanellopoulos, Dimitris

  • Author_Institution
    Univ. of Patras, Patras
  • fYear
    2007
  • fDate
    21-23 Nov. 2007
  • Firstpage
    1602
  • Lastpage
    1607
  • Abstract
    This paper firstly provides a review on the various methodologies that have tried to handle the problem of learning from data sets with an unbalanced class distribution. Finally, it presents an experimental study of these methodologies with the local application of Metacost algorithm and it concludes that such a framework can be a more effective solution to the problem.
  • Keywords
    belief networks; data analysis; learning (artificial intelligence); data sets; lazy MetaCost naive Bayes; machine-learning methods; unbalanced class distribution; Costs; Credit cards; Information technology; Laboratories; Machine learning; Machine learning algorithms; Mathematics; Medical diagnosis; Programming; Training data;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Convergence Information Technology, 2007. International Conference on
  • Conference_Location
    Gyeongju
  • Print_ISBN
    0-7695-3038-9
  • Type

    conf

  • DOI
    10.1109/ICCIT.2007.82
  • Filename
    4420482
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2627905