Naive bayesian classifier committees

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

Volumn 1398, Issue , 1998, Pages 196-207

  Zheng, Zijian ^a  

^a DEAKIN UNIVERSITY   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

DECISION TREES; FORECASTING;

BAYESIAN CLASSIFIER; CARTESIAN PRODUCTS; CLASSIFICATION TASKS; COMMITTEE MEMBERS; EFFECTIVE APPROACHES; NAIVE BAYESIAN; NAIVE BAYESIAN CLASSIFIER; PREDICTION ACCURACY;

MACHINE LEARNING;

EID: 84957081220     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/bfb0026690     Document Type: Conference Paper

References (24)

1. Almuallim, H. and Dietterich, T.G.: Efficient algorithms for identifying relevant features. Proceedings of the 9th Canadian Conference on Artificial Intelligence. Vancouver, BC: Morgan Kaufmann (1992) 38-45.
2. Breiman, L., Friedman, J.H., Olshen, R.A., and Stone, C.J.: Classification And Regression Trees. Belmont, CA: Wadsworth (1984).
3. Breiman, L.: Bagging predictors. Machine Learning. 24 (1996) 123-140.
4. Cestnik, B., Kononenko, I., and Bratko, I.: ASSISTANT 86: A knowledge-elicitation tool for sophisticated users. In I. Bratko & N. LavraZ (Eds.), Progress in Machine Learning - Proceedings of the 2nd European Working Session on Learning (EWSL87). Wilmslow, UK: Sigma Press (1987) 31-45.
5. Chatfield, C.: Statistics for Technology: A Course in Applied Statistics. London: Chapman and Hall (1978).
6. Domingos, P. and Pazzani, M.: Beyond independence: Conditions for the optimality of the simple Bayesian classifier. Proceedings of the 13th International Conference on Machine Learning. San Francisco, CA: Morgan Kaufmann (1996) 105-112.
7. Duda, R.O. and Hart, P.E.: Pattern Classification and Scene Analysis. New York: John Wiley (1973).
8. Fayyad, U.M. and Irani, K.B.: Multi-interval discretization of continuous-valued attributes for classification learning. Proceedings of the 13th International Joint Conference on Artificial Intelligence. Morgan Kaufmann (1993) 1022-1027.
9. Freund, Y. and Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. Unpublished manuscript, available from the authors' home pages ("http://www.research.att.com/{"yoav,"schapire}") (1996a).
10. Freund, Y. and Schapire, R.E.: Experiments with a new boosting algorithm. Proceedings of the 13th International Conference on Machine Learning. Morgan Kaufmann (1996b) 148-156.
11. John, G.H., Kohavi, It., and Pfleger, K.: Irrelevant features and the subset selection problem. Proceedings of the 11th International Conference on Machine Learning. San Francisco, CA: Morgan Kaufmann (1994) 121-129.
12. Kira, K. and Rendell, L.A.: The feature selection problem: Traditional methods and a new algorithm. Proceedings of the 10th National Conference on Artificial Intelligence. Menlo Park, CA: AAAI Press/Cambridge, MA: MIT Press (1992) 129-134.
13. Kohavi, R.: A study of cross-validation and bootstrap for accuracy estimation and model selection. Proceedings of the 14th International Joint Conference on Artificial Intelligence. San Mateo, CA: Morgan Kaufmann (1995) 1137-1143.
14. Kohavi, R.: Scaling up the accuracy of naive-Bayes classifiers: A decision-tree hybrid. Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining. Menlo Park, CA: The AAAI Press (1996) 202-207.
15. Kononenko, I.: Comparison of inductive and naive Bayesian learning approaches to automatic knowledge acquisition. In B. Wielinga et al. (Eds.), Current Trends in Knowledge Acquisition. Amsterdam: IOS Press (1990).
16. Kononenko, I.: Semi-naive Bayesian classifier. Proceedings of European Conference on Artificial Intelligence (1991) 206-219.
17. Langley, P., Iba, W.F., and Thompson, K.: An analysis of Bayesian classifiers. Proceedings of the 10th National Conference on Artificial Intelligence. Menlo Park, CA: The AAAI Press (1992) 223-228.
18. Langley, P.: Selection of relevant features in machine learning. Proceeding of the AAAI Fall Symposium on Relevance, New Orleans, LA: The AAAI Press (1994).
19. Langley, P. and Sage, S.: Induction of selective Bayesian classifiers. Proceedings of the 10th Conference on Uncertainty in Artificial Intelligence. Seattle, WA: Morgan Kaufmann (1994) 339-406.
20. Merz, C.J. and Murphy, P.M.: UCI Repository of Machine Learning Databases [http://www.ics.uci.edu/Thalearn/MLRepository.html]. Irvine, CA: University of California, Department of Information and Computer Science (1997).
21. Pazzani, M.J.: Constructive induction of Cartesian product attributes. Proceedings of the Conference, ISIS '96: Information, Statistics and Induction in Science. Singapore: World Scientific (1996) 66-77.
22. Quinlan, J.R.: Bagging, boosting, and C4.5. Proceedings of the 13th National Conference on Artificial Intelligence, Menlo Park: The AAAI Press (1996) 725-730.
23. Schapire, R.E., Freund, Y., Bartlett, P., and Lee W.S.: Boosting the margin: A new explanation for the effectiveness of voting methods. Proceedings of the 11th International Conference on Machine Learning. Morgan Kaufmann (1997) 322-330.
24. Ting, K.M.: Discretization of continuous-valued attributes and instance-based learning (Technical Report 491). Sydney, Australia: University of Sydney, Gasser Department of Computer Science (1994).