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  • 标题:Application of Rough Classification of Multi-objective Extension Group Decision-making under Uncertainty
  • 本地全文:下载
  • 作者:Jia-jun ZHU ; Jian-guo ZHENG ; Chao-yong QIN
  • 期刊名称:Management Science and Engineering
  • 印刷版ISSN:1913-0341
  • 电子版ISSN:1913-035X
  • 出版年度:2009
  • 卷号:3
  • 期号:3
  • 页码:38-53
  • DOI:10.3968/j.mse.1913035X20090303.005
  • 出版社:Canadian Research & Development Center of Sciences and Cultures
  • 摘要:On account of the problem of incomplete information system in classification of extension group decision-making, this paper studies attribution reduction with decision-making function based on the group interaction and individual preferences assembly for achieving the goal of rough classification of multi-objective extension group decision-making under uncertainty. Then, this paper describes the idea and operating processes of multi-objective extension classification model in order to provide decision-makers with more practical, easy to operate and objective classification. Finally, an example concerning practical problem is given to demonstrate the classification process. Combining by extension association and rough reduction, this method not only takes the advantages of dynamic classification in extension decision-making, but also achieves the elimination of redundant attributes, conducive to the promotion on the accuracy and the reliability of the classification results in multi-objective extension group decision-making. Keywords: extension group decision-making; matter-element analysis; extension association; rough set; attribution reduction
  • 其他摘要:On account of the problem of incomplete information system in classification of extension group decision-making, this paper studies attribution reduction with decision-making function based on the group interaction and individual preferences assembly for achieving the goal of rough classification of multi-objective extension group decision-making under uncertainty. Then, this paper describes the idea and operating processes of multi-objective extension classification model in order to provide decision-makers with more practical, easy to operate and objective classification. Finally, an example concerning practical problem is given to demonstrate the classification process. Combining by extension association and rough reduction, this method not only takes the advantages of dynamic classification in extension decision-making, but also achieves the elimination of redundant attributes, conducive to the promotion on the accuracy and the reliability of the classification results in multi-objective extension group decision-making.
  • 关键词:extension group decision-making;matter-element analysis;extension association;rough set;attribution reduction
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