Aggregating classifiers with ordinal response structure

Journal of Statistical Computation and Simulation

Volumn 75, Issue 5, 2005, Pages 391-408

  Tutz, Gerhard ^a   Hechenbichler, Klaus ^a  

^a UNIVERSITY OF MUNICH   (Germany)

Author keywords

Aggregating classifiers; Bagging; Boosting; Ordinal response

Indexed keywords

EID: 27844513324     PISSN: 00949655     EISSN: None     Source Type: Journal    
DOI: 10.1080/00949650410001729481     Document Type: Article

References (26)

1. Breiman, L., Friedman, J., Olshen, R. and Stone, C., 1984, Classification and Regression Trees (Boca Raton-London-New York-Washington, DC: Chapman & Hall).
2. McLachlan, G., 1992, Discriminant Analysis and Statistical Pattern Recognition (New York: Wiley).
3. Ripley, B., 1996, Pattern Recognition and Neural Networks (Cambridge: University Press).
4. Hastie, T., Tibshirani, R. and Friedman, J., 2001, The Elements of Statistical Learning (New York: Springer).
5. Breiman, L., 1996, Bagging predictors. Machine Learning, 24, 123-140.
6. Breiman, L., 1998, Arcing classifiers. Annals of Statistics, 26, 801-849.
7. Freund, Y., 1995, Boosting a weak learning algorithm by majority. Information and Computation, 121, 256-285.
8. Freund, Y. and Schapire, R., 1996, Experiments with a new boosting algorithm. Machine Learning: Proceedings of the Thirteenth International Conference, Morgan Kauffman, San Francisco.
9. Friedman, J. and Hall, P., 2000, On bagging and nonlinear estimation. Technical Report, Stanford University.
10. Bühlmann, P. and Yu, B., 2002a, Analyzing bagging. Annals of Statistics, 30, 927-961.
11. Bühlmann, P., 2003, Bagging, subagging and bragging for improving some prediction algorithms. Recent Advances and Trends in Nonparametric Statistics, in press.
12. Hothorn, T. and Lausen, B., 2003, Double-bagging: combining classifiers by bootstrap aggregation. Pattern Recognition, 36, 1303-1309.
13. Schapire, R., Freund, Y., Bartlett, P. and Lee, W., 1998, Boosting the margin: a new explanation for the effectiveness of voting methods. A nnals of Statistics, 26, 1651-1686.
14. Friedman, J., 1999, Stochastic gradient boosting. Technical Report, Stanford University.
15. Friedman, J., Hastie, T. and Tibshirani, R., 2000, Additive logistic regression: a statistical view of boosting. Annals of Statistics, 28, 337-374.
16. Friedman, J., 2001, Greedy function approximation: a gradient boosting machine. Technical Report, Stanford University.
17. 2-boosting and matching pursuit with trees and tree-type basis functions. Research Report 109, ETH Zürich.
18. 2-loss: regression and classification. Journal of the American Statistical Association, in press.
19. Dudoit, S., Fridlyand, J. and Speed, T., 2002, Comparison of discrimination methods for the classification of tumors using gene expression data. Journal of the American Statistical Association, 97, 77-87.
20. Dettling, M. and Bühlmann, P., 2003, Boosting for tumor classification with gene expression data. Bioinformatics, 19, 1061-1069.
21. Schapire, R., 2002, The boosting approach to machine learning: an overview. MSRI Workshop on Nonlinear Estimation and Classification (New York: Springer).
22. Dietterich, T., 2000, An experimental comparison of three methods for constructing ensembles of decision trees: bagging, boosting and randomization. Machine Learning, 40, 139-157.
23. Bauer, E. and Kohavi, R., 1999, An empirical comparison of voting classification algorithms: bagging, boosting and variants. Machine Learning, 36, 105-142.
24. Anderson, J. and Philips, P., 1981, Regression, discrimination and measurement models for ordered categorical variables. Applied Statistics, 30, 22-31.
25. Rudolfer, S., Watson, P. and Lesaffre, E., 1995, Are ordinal models useful for classification? A revised analysis. Journal of Statistical Computation and Simulation, 52, 105-132.
26. Fahrmeir, L., Hamerle, A. and Tutz, G., 1996, Multivariate statistische Verfahren (Berlin-New York: de Gruyter).