• DocumentCode
    3533320
  • Title

    APPrOVE: Application-oriented validation and evaluation of supervised learners

  • Author

    Lavesson, Niklas ; Davidsson, Paul

  • Author_Institution
    Sch. of Comput., Blekinge Inst. of Technol., Ronneby, Sweden
  • fYear
    2010
  • fDate
    7-9 July 2010
  • Firstpage
    150
  • Lastpage
    155
  • Abstract
    Learning algorithm evaluation is usually focused on classification performance. However, the characteristics and requirements of real-world applications vary greatly. Thus, for a particular application, some evaluation criteria are more important than others. In fact, multiple criteria need to be considered to capture application-specific trade-offs. Many multi-criteria methods can be used for the actual evaluation but the problems of selecting appropriate criteria and metrics as well as capturing the trade-offs still persist. This paper presents a framework for application-oriented validation and evaluation (APPrOVE). The framework includes four sequential steps that together address the aforementioned problems and its use in practice is demonstrated through a case study.
  • Keywords
    learning (artificial intelligence); program verification; software performance evaluation; application oriented validation and evaluation; application specific trade off; learning algorithm evaluation; supervised learner; Classification algorithms; Image databases; Image recognition; Stress; Supervised learning; Text categorization; classification; evaluation; supervised learning;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Systems (IS), 2010 5th IEEE International Conference
  • Conference_Location
    London
  • Print_ISBN
    978-1-4244-5163-0
  • Electronic_ISBN
    978-1-4244-5164-7
  • Type

    conf

  • DOI
    10.1109/IS.2010.5548402
  • Filename
    5548402