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IMPROVING NAIVE BAYES FOR CLASSIFICATION
L. Jiang, Z. Cai, and D. Wang
References
[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] 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] N. Friedman, D. Geiger, & M. Goldszmidt, Bayesian network classifiers,
Machine Learning, 29
, 1997, 131–163.
[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] 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] G.I. Webb, J. Boughton, & Z. Wang, Not so naive bayes: Aggregating one-dependence estimators,
Machine Learning, 58
, 2005, 5–24.
[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] 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] 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] 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] L. Jiang, H. Zhang, Z. Cai, & J. Su, Evolutional naiveBayes,
Proc. of the 1st International Symposium on Intelligent Computation and its Applications
, ISICA 2005, Wuhan, China, 344–350.
[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] W. Deng, G. Wang, & Y. Wang, Weighted naive Bayes classification algorithm based on rough set,
Computer Science, 34
, 2007, 204–206.
[14] M. Hall, A decision tree-based attribute weighting filter for naive Bayes,
Knowledge-Based Systems, 20
, 2007, 120–126.
[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] 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] 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] C. Elkan,
Boosting and naive Bayesian learning
Technical Report CS97-557, University of California, San Diego, 1997.
[19] I. Kononenko, Semi-naive Bayesian classifier,
Proc. of European Conference on Artificial Intelligence
, Porto, Portugal, 1991, 206–219.
[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] C. Merz, P. Murphy, & D. Aha, UCI repository of machine learning databases, Department of ICS, University of California, Irvine, http://www.ics.uci.edu/mlearn/MLRepository.html, 1997.
[22] I.H. Witten & E. Frank,
Data mining: Practical machine learning tools and techniques
, Second Edition (San Francisco: Morgan Kaufmann, 2005), http://prdownloads.sourceforge.net/weka/datasets-UCI.jar.
[23] C. Nadeau & Y. Bengio, Inference for the generalization error,
Advances in Neural Information Processing Systems, 12
, 1999, 307–313.
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Abstract
DOI:
10.2316/Journal.202.2010.3.202-2747
From Journal
(202) International Journal of Computers and Applications - 2010
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