• DocumentCode
    2217410
  • Title

    Comprehensive Evaluation of E-commerce Websites Based on PCA and SVM

  • Author

    Caiqing, Zhang ; Ming, Lin

  • Author_Institution
    Sch. of Bus. Adm., North China Electr. Power Univ., Baoding, China
  • Volume
    2
  • fYear
    2008
  • fDate
    19-21 Dec. 2008
  • Firstpage
    355
  • Lastpage
    358
  • Abstract
    With the increasing use of e-commerce Web sites, it is gaining more and more attention. The evaluation of e-commerce Web sites becoming the focus of study. This paper design a comprehensive evaluation indicator system. Adopting principal component analysis method to simplify the indicator system. A evaluation model of customer relationship management system based on support vector machine was presented. Using the idea of decision binary tree, then makes the cut date to be the input information of classifier, and establish multi-classification model. By using the combination of the two methods, we will acquire a more objective and believable evaluation result. The simulation result shows that the model has better accuracy of the classification.
  • Keywords
    Web sites; customer relationship management; decision trees; electronic commerce; pattern classification; principal component analysis; support vector machines; comprehensive evaluation indicator system; customer relationship management; decision binary tree; e-commerce Web site; multiclassification model; principal component analysis; support vector machine; Industrial engineering; Information management; Innovation management; Principal component analysis; Support vector machines; Comprehensive Evaluation; E-commerce Websites; Principal Component Analysis; Support Vector Machine;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Information Management, Innovation Management and Industrial Engineering, 2008. ICIII '08. International Conference on
  • Conference_Location
    Taipei
  • Print_ISBN
    978-0-7695-3435-0
  • Type

    conf

  • DOI
    10.1109/ICIII.2008.194
  • Filename
    4737662