• DocumentCode
    3017942
  • Title

    Comparison among Methods of Ensemble Learning

  • Author

    Shaohua Wan ; Hua Yang

  • Author_Institution
    Sch. of Inf. & Safety Eng., Zhongnan Univ. of Econ. & Law, Wuhan, China
  • fYear
    2013
  • fDate
    2-5 July 2013
  • Firstpage
    286
  • Lastpage
    290
  • Abstract
    Ensemble learning refers to a collection of methods that learn a target function by training a number of individual learners and combining their predictions. We explore four popular methods (bagging, boosting, stacking and random forest) of combining their outputs, for classification and training time and regression problems. Following this, experimental evaluations are performed on UCI datasets.
  • Keywords
    data analysis; learning (artificial intelligence); regression analysis; UCI datasets; bagging; boosting; ensemble learning; random forest; regression problems; stacking; target function; training time; Bagging; Boosting; Classification algorithms; Stacking; Training; Training data; Vegetation; Bagging; Boosting; Random Forest; Stacking;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Biometrics and Security Technologies (ISBAST), 2013 International Symposium on
  • Conference_Location
    Chengdu
  • Print_ISBN
    978-0-7695-5010-7
  • Type

    conf

  • DOI
    10.1109/ISBAST.2013.50
  • Filename
    6597704
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3017942