• DocumentCode
    2547544
  • Title

    Prediction model for region competitiveness based on entropy computing

  • Author

    Xinguang, Peng ; Lei, Chen ; Renzhong, He

  • Author_Institution
    Coll. of Comput. & Software, Taiyuan Univ. of Technol., Taiyuan, China
  • fYear
    2010
  • fDate
    16-18 April 2010
  • Firstpage
    526
  • Lastpage
    529
  • Abstract
    Prediction model of region competitiveness gives an objective understanding of the developments and advantages of the regions. It helps to make the strategies of competition of the regions and to accelerate the development of the regions. In the paper, we construct the evaluation index system for the region competitiveness of Shanxi province in China with local actual conditions on the basis of diamond theory. Then the classifications of individual region are determined by clustering algorithm. Finally, an alternative prediction and evaluation model for the region competitiveness of Shanxi province is created by using entropy computing and classification decision tree algorithm. Furthermore, we use the results to make further countermeasures to enhance the competitiveness of regions for Shanxi province.
  • Keywords
    decision trees; economic indicators; entropy; pattern classification; pattern clustering; town and country planning; China; Shanxi province; classification decision tree algorithm; clustering algorithm; diamond theory; entropy computing; evaluation index system; prediction model; region competitiveness; Cities and towns; Classification algorithms; Classification tree analysis; Clustering algorithms; Decision trees; Educational institutions; Entropy; Helium; Machine learning algorithms; Predictive models; classifier; competitiveness; decision; entropy; prediction model;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Information Management and Engineering (ICIME), 2010 The 2nd IEEE International Conference on
  • Conference_Location
    Chengdu
  • Print_ISBN
    978-1-4244-5263-7
  • Electronic_ISBN
    978-1-4244-5265-1
  • Type

    conf

  • DOI
    10.1109/ICIME.2010.5477788
  • Filename
    5477788