• DocumentCode
    479442
  • Title

    Grading Cost Sensitive Models

  • Author

    Kotsiantis, Sotiris ; Kanellopoulos, Dimitris

  • Author_Institution
    Dept. of Comput. Sci. & Technol., Univ. of Peloponnese, Tripoli
  • Volume
    1
  • fYear
    2008
  • fDate
    11-13 Nov. 2008
  • Firstpage
    663
  • Lastpage
    668
  • Abstract
    A learner induced from an imbalanced dataset has a low error rate for the majority class and an undesirable error rate for the minority class. This paper provides a study on the various methodologies that have tried to handle this problem. Finally, it presents an experimental study of these methodologies with a proposed grading cost-sensitive ensemble and it concludes that this ensemble is a more effective solution to the problem.
  • Keywords
    learning (artificial intelligence); pattern classification; error rate; grading cost-sensitive ensemble; imbalanced dataset; Bayesian methods; Classification tree analysis; Computer science; Costs; Decision trees; Error analysis; Information technology; Machine learning; Testing; Training data; classification; data mining; imbalanced dataset; machine learning;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Convergence and Hybrid Information Technology, 2008. ICCIT '08. Third International Conference on
  • Conference_Location
    Busan
  • Print_ISBN
    978-0-7695-3407-7
  • Type

    conf

  • DOI
    10.1109/ICCIT.2008.103
  • Filename
    4682102
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=479442