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证据理论在不完备信息系统中的应用 被引量:1

Application of Evidence in Incomplete Information System
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摘要 证据理论是一种重要的不确定性推理方法。将 D S证据理论应用于不完备信息系统中 ,通过对 D S证据理论和贝叶斯理论应用在不完备信息系统的比较 ,表明了 D S理论在不完备信息系统的规则提取方面具有更大的优越性 ,并通过实验证实了这一点。 DS evidence theory is an important method in uncertainty reasoning.This paper discusses the application of DS evidence theory in incomplete information system,which shows that DS evidence theory has more advantages in rulesextracting of incomplete information system by the way of comparing the application of DS evidence theory with Bayesian theory in incomplete information system,and validates it by experiment.
作者 张睿 梁吉业
机构地区 山西大学
出处 《电脑开发与应用》 2004年第4期2-4,共3页 Computer Development & Applications
基金 国家自然科学基金资助(编号:6 0 2 75 0 19) 山西省自然科学基金资助 (编号:2 0 0 310 36 )
关键词 人工智能 不确定性推理 信息融合 证据理论 不完备信息系统 D-S证据理论 DS evidence theory,basic probability,assignment function,combination function,incomplete information system,Bayesian theory
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参考文献6

  • 1A P Dempster. Upper and lower probabilities induced by amultivalued mapping[J]. Annals of Mathematical Staffstics,1967,38:325-339.
  • 2G Shafer. A mathematical Theory of Evidence[M]. Princeton University Press ,1976.
  • 3Sarabjot S Anand,David A Bell, John G Hughes. EDM.A general Framework for Data Mining based on EvidenceTheory[J]. Data &Knowledge Engineering, 1996,18: 189-223.
  • 4A P Dempster.Upper and lower probabilities induced by a multi-valued mapping[J].Annals of Mathematical Statistics,1967,38:325-339
  • 5G Shafer.A mathematical Theory of Evidence[M].Princeton University Press,1976
  • 6Sarabjot S Anand,David A Bell,John G Hughes.EDM:A general Framework for Data Mining based on Evidence Theory[J].Data &Knowledge Engineering,1996,18:189-223.

同被引文献7

  • 1黄鵾,陈森发,周振国,张文红.基于粗集理论和证据理论的多源信息融合方法[J].信息与控制,2004,33(4):422-425. 被引量:9
  • 2张睿,梁吉业.不完备决策表的一种知识约简算法[J].计算机应用研究,2004,21(10):22-23. 被引量:7
  • 3PAWLAK Z.Rough sets:theoretical aspects of reasoning about data[M].Dordrecht:Kluwer Academic Publishers,1991:89-95.
  • 4ANAND S S,BELL D A,HUGHES J G.EDM:a general framework for data mining based on evidence theory[J].Data & Knowledge Engineering,1996,18(3):189-223.
  • 5LIANG Jiye,XU Zongben.The algorithm on knowledge reduction in incomplete information systems[J].International Journal of Uncertainty,Fuzziness and Knowledge-based Systems,2002,10(1):952-1103.
  • 6DEMPSTER A P.Upper and lower probability inferences based on a sample from a finite univariate population[J].Biometrika,1967,54(3):515-528.
  • 7SHAFER G.A mathematical theory of evidence[M].[S.l.]:Princeton University Press,1976.

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