• DocumentCode
    3443924
  • Title

    An ensemble learning algorithm based on Lasso selection

  • Author

    Chen, Kai ; Jin, Yang

  • Author_Institution
    Sch. of Stat., Renmin Univ. of China, Beijing, China
  • Volume
    1
  • fYear
    2010
  • fDate
    29-31 Oct. 2010
  • Firstpage
    617
  • Lastpage
    620
  • Abstract
    Ensemble learning, especially selective ensemble learning is now becoming more and more popular in the field of machine learning. This paper introduces a new ensemble algorithm, named Lasso-Bagging Trees ensemble algorithm. This algorithm is in order to improve the whole learning ability, which is a combination of tree predictors and this method chooses and ensembles trees based on the shrinkage estimation of lasso technology. Compared with a series of other learning algorithms, it demonstrates better generalization ability and higher efficiency.
  • Keywords
    decision trees; estimation theory; generalisation (artificial intelligence); learning (artificial intelligence); statistical analysis; Lasso Bagging trees ensemble algorithm; generalization; learning ability; machine learning; selective ensemble learning; shrinkage estimation; tree predictor; Algae; Artificial intelligence; Bagging; Bootstrap; Decision Tree; Selective Ensemble;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Computing and Intelligent Systems (ICIS), 2010 IEEE International Conference on
  • Conference_Location
    Xiamen
  • Print_ISBN
    978-1-4244-6582-8
  • Type

    conf

  • DOI
    10.1109/ICICISYS.2010.5658515
  • Filename
    5658515