• DocumentCode
    3440927
  • Title

    Neural Network Ensemble Based on K-Means Clustering Individual Selection and Application for Software Reliability Prediction

  • Author

    Li Kewen ; Zhao Kang ; Liu Wenying

  • Author_Institution
    Coll. of Comput. & Commun. Eng., China Univ. of Pet. Qingdao, Qingdao, China
  • fYear
    2013
  • fDate
    3-4 Dec. 2013
  • Firstpage
    131
  • Lastpage
    135
  • Abstract
    A novel neural network ensemble is proposed and applied to the software reliability prediction in the paper which based on the K-means clustering individual selection. First, multiple neural networks are generated by changing the structure of the neural network, then individual selection ensemble is made with K-means clustering method, and finally the outputs of these selected individuals by entropy weight method are integrated. The new method has been proved superior in software reliability prediction by experimental comparison.
  • Keywords
    neural nets; pattern clustering; prediction theory; software reliability; K-means clustering individual selection; entropy weight method; neural network ensemble; software reliability prediction; Accuracy; Algorithm design and analysis; Clustering algorithms; Entropy; Neural networks; Prediction algorithms; Software reliability; ensemble model; individual selection; neural network; software reliability;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Software Engineering (WCSE), 2013 Fourth World Congress on
  • Conference_Location
    Hong Kong
  • Print_ISBN
    978-1-4799-2882-8
  • Type

    conf

  • DOI
    10.1109/WCSE.2013.25
  • Filename
    6754275