International Journal of Fuzzy Logic and Intelligent Systems

  • 제10권4호
  • /
  • Pages.253-258
  • /
  • 2010
  • /
  • 1598-2645(pISSN)
  • /
  • 2093-744X(eISSN)
한국지능시스템학회 (Korean Institute of Intelligent Systems)
권호 표지
DOI QR코드

DOI QR Code

An Extended Version of the CPT-based Estimation for Missing Values in Nominal Attributes

  • Ko, Song (School of Computer Science and Engineering, Chung-Ang University) ;
  • Kim, Dae-Won (School of Computer Science and Engineering, Chung-Ang University)
  • 투고 : 2010.07.28
  • 심사 : 2010.12.10
  • 발행 : 2010.12.25
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

The causal network represents the knowledge related to the dependency relationship between all attributes. If the causal network is available, the dependency relationship can be employed to estimate the missing values for improving the estimation performance. However, the previous method had a limitation in that it did not consider the bidirectional characteristic of the causal network. The proposed method considers the bidirectional characteristic by applying prior and posterior conditions, so that it outperforms the previous method.

키워드

참고문헌

  1. A.Rogier T.Donders, Geert J.M.G. van der Heijden, Theo Stijnen, Karel G.M. Moons, Review: “A gentle introduction to imputation of missing values”, Journal of Clinical Epidemiology Vol.59, pp.1087-1091, 2006. https://doi.org/10.1016/j.jclinepi.2006.01.014
  2. Alireza Farhangfar, Lukasz A.Kurgan, Member, IEEE, and Witold Pedrycz, Fellow, IEEE, “A Novel Framework for Imputation of Missing Valuesin Databases”, IEEE Transaction on Systems, Man, and Cybernetics-PART A : System and Humans, Vol.37, NO.5, pp.692-709, SEPTEMBER 2007. https://doi.org/10.1109/TSMCA.2007.902631
  3. Marco Ramoni and Paola Sebastiani, “Robust Learning with Missing Data”, Machine Learning, Vol.45, pp.147-170, 2001. https://doi.org/10.1023/A:1010968702992
  4. S.F.Buck, “A Method of Estimation of Missing Val-ues in Multivariate Data suitable for use with an Electronic Computer”, Journal of the Royal Statisti-cal Society. Series B (Methodological), Vol.22, NO.2, pp.302-306, 1960.
  5. Z.Ghahramani and M.I.Jordan, “Mixture models for learning from incomplete data”, Computational learning theory and natural learning systems : VolumeIV, MIT Press, pp.67-85,1997.
  6. Marco Di Zio, Mauro Scanu, Lucia Coppola, Orietta Luza and Alessandra Ponti, “Bayesian Network for Imputation”, Journal of the Royal Statistical Society : SeriesA, Vol.167, Part2, pp.309-322, 2004. https://doi.org/10.1046/j.1467-985X.2003.00736.x
  7. Shulin Yang and Kuo-Chu Chang, “Comparison of Score Metrics for Bayesian Network Learning”, IEEE Transaction on Systems, Man and Cybernetics- PARTA : Systems and Humans, Vol.32, No.3, pp.419- 428, May2002 https://doi.org/10.1109/TSMCA.2002.803772
  8. Judea Pearl, Probabilistic Reasoning in Intelligent Systems : Network of Plausible Inference, 1988.