• DocumentCode
    2845980
  • Title

    Boosting the Performance of CBR Applications with jCOLIBRI

  • Author

    Recio-Garcia, Juan A. ; Diaz-Agudo, Belen ; Gonzalez-Calero, Pedro Antonio

  • Author_Institution
    Dept. of Software Eng. & Artificial Intell., Univ. Complutense de Madrid, Madrid, Spain
  • fYear
    2009
  • fDate
    2-4 Nov. 2009
  • Firstpage
    276
  • Lastpage
    283
  • Abstract
    jCOLIBRI is currently a reference platform in the CBR community for building CBR systems that includes facilities to design different types of CBR applications. In this paper we focus in some recently included tools that allow the improvement of performance of previously designed applications. These optimization tools mainly facilitate to adjust features on large case bases like clustering and noise reduction techniques, and to adjust processes like refine similarity metrics through case base visualization, parallelization of retrieval or distribution of the case base and reasoning thought different agents. We present the tools and exemplify how to use them in a real scenario. We have developed an experiment for the automatic classification of a textual case base made of 1500 academic journals belonging to 20 different areas.
  • Keywords
    case-based reasoning; optimisation; CBR applications; case-based reasoning; clustering; jCOLIBRI; noise reduction; optimization; similarity metrics; textual case classification; Application software; Artificial intelligence; Boosting; Buildings; Noise reduction; Recommender systems; Software engineering; Software prototyping; Software tools; Visualization; CBR; Case-Based Reasoning; jCOLIBRI;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Tools with Artificial Intelligence, 2009. ICTAI '09. 21st International Conference on
  • Conference_Location
    Newark, NJ
  • ISSN
    1082-3409
  • Print_ISBN
    978-1-4244-5619-2
  • Electronic_ISBN
    1082-3409
  • Type

    conf

  • DOI
    10.1109/ICTAI.2009.130
  • Filename
    5365080