Table of Contents Author Guidelines Submit a Manuscript
Mathematical Problems in Engineering
Volume 2014 (2014), Article ID 470821, 11 pages
http://dx.doi.org/10.1155/2014/470821
Research Article

Extracting Credible Dependencies for Averaged One-Dependence Estimator Analysis

1Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China
2State Key Laboratory of Computer Science, Beijing 100080, China
3School of Mathematics and Information, Shanghai Lixin University of Commerce, Shanghai 210620, China
4Medical College, Jilin University, Changchun 130021, China

Received 8 April 2014; Revised 25 May 2014; Accepted 26 May 2014; Published 17 June 2014

Academic Editor: Yang Xu

Copyright © 2014 LiMin Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. D. Dash and G. F. Cooper, “Exact model averaging with naive Bayesian classifiers,” in Proceedings of the 19th International Conference on Machine Learning, pp. 91–98, Sydney, Australia, July 2002.
  2. E. Frank, M. Hall, and B. Pfahringer, “Locally weighted naive Bayes,” in Proceedings of the 19th Conference on Uncertainty in Artificial Intelligence, pp. 249–256, Acapulco, NM, USA, August 2003.
  3. F. Zheng and G. I. Webb, “Finding the right family: parent and child selection for averaged one dependence estimators,” in Proceedings of the 18th European Conference on Machine Learning, pp. 490–501, Warsaw, Poland, September 2007.
  4. F. Zheng and G. I. Webb, “Efficient lazy elimination for averaged one-dependence estimators,” in Proceedings of the 23rd International Conference on Machine Learning, pp. 1113–1120, Pittsburgh, Pa, USA, June 2006. View at Scopus
  5. A. Z. Nayyar, C. Jesus, and G. I. Webb, “Alleviating naive bayes attribute independence assumption by attribute weighting,” The Journal of Maching Learning Research, vol. 14, no. 6, pp. 1113–1120, 2006. View at Google Scholar
  6. P. Langley and S. Sage, “Induction of selective Bayesian classifiers,” in Proceedings of the 10th Conference on Uncertainty in Artificial Intelligence, pp. 399–406, Seattle, Wash, USA, July 1994.
  7. M. J. Pazzani, “Constructive induction of Cartesian product attributes,” in Proceedings of the Information, Statistics and Induction in Science Conference, pp. 66–77, July 1996.
  8. F. Zheng, G. I. Webb, P. Suraweera, and L. Zhu, “Subsumption resolution: an efficient and effective technique for semi-naive Bayesian learning,” Machine Learning, vol. 87, no. 1, pp. 93–125, 2012. View at Publisher · View at Google Scholar · View at Scopus
  9. W. W. Armstrong, “Dependency structures of data base relationships,” in Proceedings of the IFIP Congress, pp. 580–583, 1974. View at Zentralblatt MATH · View at MathSciNet
  10. L. M. Wang, G. F. Yao, and X. Li, “Extracting logical rules and attribute subset from confidence domain,” Information, vol. 15, no. 1, pp. 173–180, 2012. View at Google Scholar · View at MathSciNet · View at Scopus
  11. L. M. Wang and G. F. Yao, “Bayesian network inference based on functional dependency mining of relational database,” Information, vol. 15, no. 6, pp. 2441–2446, 2012. View at Google Scholar · View at Scopus
  12. R. Kohavi and D. Wolpert, “Bias plus variance decomposition for zero-one loss functions,” in Proceedings of the 13th European Conference on Machine Learning, pp. 275–283, June 1996.
  13. D. S. Moore and G. P. McCabe, Introduction to the Practice of Statistics, Michelle Julet, San Francisco, Calif, USA, 4th edition, 2002.
  14. A. Z. Nayyar and G. I. Webb, “Fast and effective single pass Bayesian learning,” in Proceedings of the 17th Pacific-Asia Conference on Knowledge Discovery and Data Mining, vol. 7818 of Lecture Notes in Computer Science, pp. 149–160, Gold Coast, Australia, April 2013. View at Publisher · View at Google Scholar
  15. B. Cestnik, “Estimating probabilities: a crucial task in machine learning,” in Proceedings of the 9th European Conference on Artificial Intelligence, pp. 147–149, Pitman, Boston, Mass, USA, August 1990.
  16. U. M. Fayyad and K. B. Irani, “Multi-interval discretization of continuous-valued attributes for classification learning,” in Proceedings of the 13th International Joint Conference on Artificial Intelligence, pp. 1022–1029, August 1993.
  17. L. M. Wang and G. F. Yao, “Learning NT Bayesian classifier based on canonical cover analysis of relational database,” Information, vol. 15, no. 1, pp. 165–172, 2012. View at Google Scholar · View at MathSciNet