A study of AdaBoost with naive Bayesian classifiers: Weakness and improvement

Computational Intelligence

Volumn 19, Issue 2, 2003, Pages 186-200

  Ming Ting, Kai ^a   Zheng, Zijian ^b  

^a MONASH UNIVERSITY   (Australia)

^b MICROSOFT   (United States)

Author keywords

Bias and variance analysis; Boosting; Naive Bayesian classification

Indexed keywords

DATA STRUCTURES; LEARNING ALGORITHMS; MATHEMATICAL MODELS; MAXIMUM LIKELIHOOD ESTIMATION; PROBABILITY DISTRIBUTIONS; VECTORS;

BAYESIAN CLASSIFIERS; BIAS AND VARIANCE ANALYSIS; BOOSTINV TECHNIQUE; DECISION TREE LEARNING; NAIVE BAYESIAN CLASSIFICATION;

LEARNING SYSTEMS;

EID: 0142103425     PISSN: 08247935     EISSN: None     Source Type: Journal    
DOI: 10.1111/1467-8640.00219     Document Type: Article

References (19)

1. Bauer, E., and R. Kohavi. 1999. An empirical comparison of voting classification algorithms: Bagging, boosting, and variants. Machine Learning, 36:105-139.
2. Blake, C., E. Keogh, and C. J. Merz. 1998 UCI repository of machine learning databases http://www.ics.uci.edu/~mlearn/MLRepository.html.
3. Breiman, L. 1996. Bias, variance, and arcing classifiers. Technical Report 460, Department of Statistics, University of California, Berkeley.
4. Cestnik, B. 1990. Estimating probabilities: A crucial task in machine learning. In Proceedings of European Conference on Artificial Intelligence, pp. 147-149.
5. Domingos, P., and M. Pazzani. 1996. Beyond independence: Conditions for the optimality of the simple Bayesian classifier. In Proceedings of the Thirteenth International Conference on Machine Learning. Morgan Kaufmann, San Francisco, pp. 105-112.
6. Duda, R. O., and P. E. Hart. 1973. Pattern Classification and Scene Analysis. John Wiley, New York.
7. Elkan, C. 1997. Boosting and Naive Bayesian Learning. Technical Report CS97-557, University of California, Davis.
8. Fayyad, U. M., and K. B. Irani. 1993. Multi-interval discretization of continuous-valued attributes for classification learning. Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence. Morgan Kaufmann, San Francisco, pp. 1022-1027.
9. Freund, Y., and R. E. Schapire. 1996. Experiments with a new boosting algorithm. In Proceedings of the Thirteenth International Conference on Machine Learning. Morgan Kaufmann, San Francisco, 148-156.
10. Friedman, J. H., T. Hastie, and R. Tibshirani. 2000. Additive logistic regression: A statistical view of boosting. Annals of Statistics, 28(2):337-374.
11. Kohavi, R. 1996, 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. AAAI Press, Menlo Park, CA, pp. 202-207.
12. Kohavi, R., and D. Wolpert. 1996. Bias plus variance decomposition for zero-one loss functions. In Proceedings of the Thirteenth International Conference on Machine Learning. Morgan Kaufmann, San Francisco, pp. 275-283.
13. Kononenko, I. 1990. Comparison of inductive and naive Bayesian learning approaches to automatic Knowledge Acquisition. In Current Trends in Knowledge Acquisition. Edited by B. Wielinga et al. IOS Press, Amsterdam, pp. 190-197.
14. Langley, P., W. F. Iba, and K. Thompson. 1992. An analysis of Bayesian classifiers. In Proceedings of the Tenth National Conference on Artificial Intelligence. AAAI Press, Menlo Park, CA, pp. 223-228.
15. Quinlan, J. R. 1993. C4.5: Program for Machine Learning. Morgan Kaufmann, San Francisco.
16. Quinlan, J. R. 1996. Bagging, boosting, and C4.5. In Proceedings of the Thirteenth National Conference on Artificial Intelligence. AAAI Press, Menlo Park, CA, pp. 725-730.
17. Ridgeway, G., D. Madigan, T. Richardson, and J. O'Kane. 1998. Interpretable boosted naive Bayes classification. In Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining, Menlo Park, CA, pp. 101-104.
18. Schapire, R. E., Y. Freund, P. Bartlett, and W. S. Lee. 1997. Boosting the margin: A new explanation for the effectiveness of voting methods. In Proceedings of the Fourteenth International Conference on Machine Learning. Morgan Kaufmann, San Francisco, pp. 322-330.
19. Schapire, R. E., and Y. Singer. 1999. Improved boosting algorithms using confidence-rated predictions. Machine Learning, 37(3):297-336.