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基于Bayesian网络的缺损数据处理方法 被引量:3

Dealing with Incomplete Data Based on Bayesian Networks
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摘要 总结了处理缺损数据的几种方法 ,并提出一种基于 Bayesian网络的缺损数据处理方法。Bayesian网络能够将样本数据和先验信息有效地结合起来。最后通过一个实例验证了该方法的有效性和正确性。 Several methods of dealing with incomplete data are summarized and a new one based on Bayesian networks is presented in this paper, which will combine data as contained in samples with prior knowledge. A case study has also been performed, demonstrating the effectiveness and correctness of this method.
出处 《华东理工大学学报(社会科学版)》 2002年第S1期41-44,共4页 Journal of East China University of Science and Technology:Social Science Edition
关键词 数据挖掘 缺损数据 BAYESIAN网络 Bayes概率 先验知识 data mining incomplete data Bayesian networks Bayes probability empirical information
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参考文献9

  • 1虞健飞,张恒喜,朱家元.数据缺失条件下的贝叶斯推断方法[J].计算机科学,2002,29(2):122-123. 被引量:4
  • 2林士敏,田凤占,陆玉昌.贝叶斯网络的建造及其在数据采掘中的应用[J].清华大学学报(自然科学版),2001,41(1):49-52. 被引量:66
  • 3卫金茂,黄道.知识发现中缺损数据的处理(II)[J].华东理工大学学报(自然科学版),2000,26(5):517-520. 被引量:5
  • 4王双成,林士敏,陆玉昌.用Bayesian网络处理具有不完整数据的问题分析[J].清华大学学报(自然科学版),2000,40(9):65-68. 被引量:5
  • 5David Maxwell Chickering,David Heckerman.Efficient Approximations for the Marginal Likelihood of Bayesian Networks with Hidden Variables[J]. Machine Learning . 1997 (2-3)
  • 6David Heckerman.Bayesian Networks for Data Mining[J]. Data Mining and Knowledge Discovery . 1997 (1)
  • 7David Heckerman,Dan Geiger,David M. Chickering.Learning Bayesian Networks: The Combination of Knowledge and Statistical Data[J]. Machine Learning . 1995 (3)
  • 8Gregory F. Cooper,Edward Herskovits.A Bayesian Method for the Induction of Probabilistic Networks from Data[J]. Machine Learning . 1992 (4)
  • 9Daniel Nikovski.Constructing Bayesian networks for medical diagnosis from incomplete and partially correct statistics. IEEE Transactions on Knowledge and Data Engineering . 2000

二级参考文献18

  • 1[1]Heckerman D. Bayesian networks for data mining [J]. Data Mining and Knowledge Discovery, 1997, 1: 79~119.
  • 2[2]Heckerman D, Geiger D, Chickering D. Learning Bayesian Networks: the combination of knowledge and statistical data [J]. Machine Learning, 1995, 20: 196~243.
  • 3[3]Geiger D, Heckerman D. A characterization of the Dirichlet distribution with applicable to learning Bayesian networks [A]. In Proceedings of Eleventh Conference on Uncertainty in Artificial Intelligence [C]. Montreal, QU, 1995. 196~207.
  • 4[4]Cooper G, Herskovits E. A Bayesian method for the induction of probabilistic networks from data [J]. Machine Learning, 1992, 9: 309~347.
  • 5[5]Dagum P, Luby M. Approximating probabilistic inference in Bayesian belief networks is NP-hard [J]. Artificial Intelligence, 1993, 60: 141~153.
  • 6[6]Chickering D. Learning equivalence classes of Bayesian-network structures [A]. In Proceedings of Twelfth Conference on Uncertainty in Artificial Intelligence [C]. Portland, OR: Morgan Kaufmann, 1996.
  • 7[7]Heckerman D, Mamdani A, Wellman M. Real-world applications of Bayesian networks [J]. Communications of the ACM, 1995, 38 (3): 24~26.
  • 8[8]Sewell W, Shah V. Social class, parental encouragement, and educational aspirations [J]. American Journal of Sociology, 1968, 73: 559~572.
  • 9[9]Spirtes P, Glymour C, Scheines R. Causation, Predication, and Search [M]. New York: Springer-Verlag, 1993.
  • 10[10]Cheeseman P, Stutz J. Bayesian classification (AutoClass): Theory and results [A]. Fayyad U, Piatesky-Shapiro G, Smyth P, et al (Eds.). Advances in Knowledge Discovery and Data Mining [C]. Menlo Park, CA: AAAI Press, 1995.

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