Learning Naïve Bayes Tree for conditional probability estimation

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 4013 LNAI, Issue , 2006, Pages 455-466

  Liang, Han ^a,b   Yan, Yuhong ^b  

^a UNIVERSITY OF NEW BRUNSWICK   (Canada)

^b NATIONAL RESEARCH COUNCIL CANADA   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; CLASSIFICATION (OF INFORMATION); COSTS; DATA ACQUISITION; DECISION THEORY; ESTIMATION; OPTIMIZATION; PROBABILITY;

AREA UNDER THE CURVE (AUC); GENERAL STRUCTURE; NAIVE BAYESIAN CLASSIFIERS; STATE-OF-ART LEARNING;

LEARNING SYSTEMS;

EID: 33746074809     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11766247_39     Document Type: Conference Paper

References (10)

1. C. Blake and C.J. Merz. Uci repository of machine learning database.
2. Charles Elkan. The foundations of cost-sensitive learning. In Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence, 1991.
3. N. Priedman, D. Geiger, and M. Goldszmidt. Bayesian network classifiers. Machine Learning, 29, 1997.
4. D. J. Hand and R. J. Till. A simple generalisation of the area under the roc curve for multiple class classification problems. Machine Learning, 45, 2001.
5. Ron Kohavi. Scaling up the accuracy of naive-bayes classifiers: a decision-tree hybrid. In Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, 1996.
6. C. Nadeau and Y. Bengio. Inference for the generalization error. Machine Learning, 52(40), 2003.
7. J. Pearl. Probabilistic Reasoning in Intelligent Systems. Morgan Kaufmann, 1988.
8. F. J. Provost and P. Domingos. Tree induction for probability-based ranking. Machine Learning, 52(30), 2003.
9. I. H. Witten and E. Frank. Data Mining -Practical Machine Learning Tools and Techniques with Java Implementation. Morgan Kaufmann, 2000.
10. H. Zhang and J. Su. Conditional independence trees. In Proceedings of the 15th European Conference on Machine Learning (ECML2004). Springer, 2004.