• DocumentCode
    2651857
  • Title

    Classification by Clusters Analysis - An Ensemble Technique in a Semi-supervised Classification

  • Author

    Jurek, Anna ; Bi, Yaxin ; Wu, Shengli ; Nugent, Chris

  • Author_Institution
    Sch. of Comput. & Math., Univ. of Ulster, Newtownabbey, UK
  • fYear
    2011
  • fDate
    7-9 Nov. 2011
  • Firstpage
    876
  • Lastpage
    878
  • Abstract
    In this work we adopt a previously introduced meta-learning classification method for semi-supervised learning problems. In our previous work we illustrated that the method is successful when applied in a supervised classification problem. In our current work the results demonstrate that following refinements made to the method it can be successfully applied to semi-supervised classification cases.
  • Keywords
    learning (artificial intelligence); pattern classification; clusters analysis; ensemble technique; meta-learning classification method; semi-supervised classification; semi-supervised learning problems; supervised classification problem; Accuracy; Bismuth; Euclidean distance; Learning systems; Stacking; Training; Training data; classifier ensemble; clustering; combining classifiers; meta learning; semi-supervised learning;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Tools with Artificial Intelligence (ICTAI), 2011 23rd IEEE International Conference on
  • Conference_Location
    Boca Raton, FL
  • ISSN
    1082-3409
  • Print_ISBN
    978-1-4577-2068-0
  • Electronic_ISBN
    1082-3409
  • Type

    conf

  • DOI
    10.1109/ICTAI.2011.137
  • Filename
    6103428