MultiBoosting: a technique for combining boosting and wagging

Machine Learning

Volumn 40, Issue 2, 2000, Pages 159-196

  Webb, Geoffrey I ^a  

^a DEAKIN UNIVERSITY   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

DATA STRUCTURES; DECISION THEORY; ERRORS; LEARNING ALGORITHMS; POISSON DISTRIBUTION; RANDOM PROCESSES; STATISTICAL METHODS;

BAGGING; BOOSTING; DECISION COMMITTEE; DECISION TREE; MULTIBOOSTING; WAGGING;

LEARNING SYSTEMS;

EID: 0034247206     PISSN: 08856125     EISSN: None     Source Type: Journal    
DOI: 10.1023/A:1007659514849     Document Type: Article

References (26)

1. Ali, K., Brunk, C., & Pazzani, M. (1994). On learning multiple descriptions of a concept. In Proceedings of Tools with Artificial Intelligence (pp. 476-483). New Orleans, LA.
2. Bauer, E. & Kohavi, R. (1999). An empirical comparison of voting classification algorithms: Bagging, boosting, and variants. Machine Learning, 36, 105-139.
3. Blake, C., Keogh, E., & Merz, C. J. (1999). UCI repository of machine learning databases. [Machine-readable data repository]. University of California, Department of Information and Computer Science, Irvine, CA.
4. Breiman, L. (1996a). Bagging predictors. Machine Learning, 24, 123-140.
5. Breiman, L. (1996b). Bias, variance, and arcing classifiers. Technical report 460. Berkeley, CA, Department of Statistics, University of California.
6. Breiman, L. (1997). Arcing the edge. Technical report 486. Berkeley, CA, Department of Statistics, University of California.
7. Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984). Classification and Regression Trees. Belmont, CA: Wadsworth International.
8. Dietterich, T. G. (1998). Approximate statistical tests for comparing supervised classification learning algorithms. Neural Computation, 10(7), 1895-1923.
9. Freund, Y. & Schapire, R. E. (1995). A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55, 119-139.
10. Freund, Y. & Schapire, R. E. (1996). Experiments with a new boosting algorithm. In Proceedings of the Thirteenth International Conference on Machine Learning (pp. 148-156). Bari, Italy: Morgan Kaufmann.
11. Friedman, J. H. (1997). On bias, variance, 0/1-loss, and the curse-of-dimensionality. Data Mining and Knowledge Discovery, 1, 55-77.
12. Friedman, J., Hastie, T., & Tibshirani, R. Additive logistic regression: A statistical view of boosting. Annals of Statistics. To appear.
13. Geman, S., Bienenstock, E., & Doursat, R. (1992). Neural networks and the bias/variance dilemma. Neural Computation, 4, 1-48.
14. Kohavi, R. & Wolpert, D. (1996). Bias plus variance decomposition for zero-one loss functions. In Proceedings of the 13th International Conference on Machine Learning (pp .275-283). Bari, Italy: Morgan Kaufmann.
15. Kong, E. B. & Dietterich, T. G. (1995). Error-correcting output coding corrects bias and variance. In Proceedings of the Twelfth International Conference on Machine Learning (pp. 313-321). Tahoe City, CA: Morgan Kaufmann.
16. Krogh, A. & Vedelsby, J. (1995). Neural network ensembles, cross validation, and active learning. G. Tesauro, D. Touretzky, & T. Leen (Eds.), Advances in Neural Information Processing Systems (Vol. 7). Boston, MA: MIT Press.
17. Nock, R. & Gascuel, O. (1995). On learning decision committees. In Proceedings of the Twelfth International Conference on Machine Learning (pp. 413-420). Taho City, CA: Morgan Kaufmann.
18. Oliver, J. J. & Hand, D. J. (1995). On pruning and averaging decision trees. In Proceedings of the Twelfth International Conference on Machine Learning (pp. 430-437). Taho City, CA: Morgan Kaufmann.
19. Quinlan, J. R. (1996). Bagging, boosting, and C4.5. In Proceedings of the Thirteenth National Conference on Artificial Intelligence (pp. 725-730). AAAI/MIT Press.
20. Rao, R. B., Gordon, D., & Spears, W. (1995). For every generalization action is there really an equal and opposite reaction? Analysis of the conservation law for generalization performance. In Proceedings of the Twelfth International Conference on Machine Learning (pp. 471-479). Taho City, CA: Morgan Kaufmann.
21. Salzberg, S. L. (1997). On comparing classifiers: Pitfalls to avoid and a recommended approach. Data Mining and Knowledge Discovery, 1, 317-327.
22. Schaffer, C. (1994). A conservation law for generalization performance. In Proceedings of the 1994 International Conference on Machine Learning. Morgan Kaufmann.
23. Schapire, R. E., Freund, Y., Bartlett, P., & Lee, W. S. (1998). Boosting the margin: A new explanation for the effectiveness of voting methods. The Annals of Statistics, 26, 1651-1686.
24. Wolpert, D. H. (1992). Stacked generalization. Neural Networks, 5, 241-259.
25. Wolpert, D. H. (1995). Off-training set error and a priori distinctions between learning algorithms. Technical Report SFI TR 95-01-003. Santa Fe, NM, The Santa Fe Institute.
26. Zheng, Z. & Webb, G. I. (1998). Multiple boosting: A combination of boosting and bagging. In Proceedings of the 1998 International Conference on Parallel and Distributed Processing Techniques and Applications (pp. 1133-1140). CSREA Press.