L. Jiang, Z. Cai, and D. Wang


  1. [1] D.M. Chickering, Learning Bayesian networks is NP-Complete, in D. Fisher & H. Lenz (Eds.), Learning from data: Artificial intelligence and statistics V (Springer-Verlag: New York, USA), 1996, 121–130.
  2. [2] L. Jiang, D. Wang, Z. Cai, & X. Yan, Survey of improving naive Bayes for classification, Proc. of the 3rd International Conference on Advanced Data Mining and Applications, ADMA 2007, LNAI 4632, (Springer Press: Harbin, China), 134–145.
  3. [3] N. Friedman, D. Geiger, & M. Goldszmidt, Bayesian network classifiers, Machine Learning, 29, 1997, 131–163.
  4. [4] E. Keogh & M. Pazzani, Learning augmented Bayesian classifiers: A comparison of distribution-based and classification-based approaches, Proc. of the International Workshop on Artificial Intelligence and Statistics, Florida, USA, 1999, 225–230.
  5. [5] H. Zhang & C. X. Ling, An improved learning algorithm for augmented naive Bayes, Proc. of the fifth pacific-asia Conference on KDD, (LNCS 2035: Hong Kong, China), 2001, 581–586.
  6. [6] G.I. Webb, J. Boughton, & Z. Wang, Not so naive bayes: Aggregating one-dependence estimators, Machine Learning, 58, 2005, 5–24.
  7. [7] H. Zhang, L. Jiang, & J. Su, Hidden naive Bayes, Proc. of the 20th National Conference on Artificial Intelligence, AAAI 2005, (AAAI Press: Pittsburgh, Pennsylvania, USA), 919–924.
  8. [8] J. Sun, C. Wang, & S. Chen, A double layer Bayesian classifier, Proc. of the 4th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2007, Vol. 1, (IEEE Computer Society Press: Haikou, China), 540–544.
  9. [9] P. Langley & S. Sage, Induction of selective Bayesian classifiers, Proc. of the Tenth Conference on Uncertainty in Artificial Intelligence, (Seattle: Washington, USA), 1994, 339–406.
  10. [10] C.A. Ratanamahatana & D. Gunopulos, Scaling up the naive Bayesian classifier: Using decision trees for feature selection, Proc. of Workshop on Data Cleaning and Preprocessing (DCAP 2002), at IEEE International Conference on Data Mining (ICDM 2002), Maebashi, Japan, 2002.
  11. [11] L. Jiang, H. Zhang, Z. Cai, & J. Su, Evolutional naive Bayes, Proc. of the 1st International Symposium on Intelligent Computation and its Applications, ISICA 2005, Wuhan, China, 344–350.
  12. [12] H. Zhang & S. Sheng, Learning weighted naive Bayes with accurate ranking, Proc. of the Fourth IEEE International Conference on Data Mining, ICDM 2004, (IEEE Computer Society Press: Brighton, UK), 567–570.
  13. [13] W. Deng, G. Wang, & Y. Wang, Weighted naive Bayes classification algorithm based on rough set, Computer Science, 34, 2007, 204–206.
  14. [14] M. Hall, A decision tree-based attribute weighting filter for naive Bayes, Knowledge-Based Systems, 20, 2007, 120–126.
  15. [15] Z. Xie, W. Hsu, Z. Liu, & M. Lee, SNNB: A selective neighbourhood based naive Bayes for lazy learning, Proc. of the Sixth Pacific-Asia Conference on KDD, (Springer: Taipei, Taiwan), 2002, 104–114.
  16. [16] E. Frank, M. Hall, & B. Pfahringer, Locally weighted naive Bayes, Proc. of the Conference on Uncertainty in Artificial Intelligence (2003), Morgan Kaufmann, Acapulco, Mexico, 2003, 249–256.
  17. [17] R. Kohavi, Scaling up the accuracy of naive-Bayes classifiers: A decision-tree hybrid, Proc. of the Second International Conference on Knowledge Discovery and Data Mining (KDD-96), (AAAI Press: Portland, Oregon, USA), 1996, 202–207.
  18. [18] C. Elkan, Boosting and naive Bayesian learning Technical Report CS97-557, University of California, San Diego, 1997.
  19. [19] I. Kononenko, Semi-naive Bayesian classifier, Proc. of European Conference on Artificial Intelligence, Porto, Portugal, 1991, 206–219.
  20. [20] L. Jiang, Z. Cai, & D. Wang, Learning averaged one-dependence estimators by instance weighting, Journal of Computational Information Systems, 4(6), 2008, 2753–2760.
  21. [21] C. Merz, P. Murphy, & D. Aha, UCI repository of machine learning databases, Department of ICS, University of California, Irvine,, 1997.
  22. [22] I.H. Witten & E. Frank, Data mining: Practical machine learning tools and techniques, Second Edition (San Francisco: Morgan Kaufmann, 2005),
  23. [23] C. Nadeau & Y. Bengio, Inference for the generalization error, Advances in Neural Information Processing Systems, 12, 1999, 307–313.

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