Adjusted estimation for the combination of classifiers

Intelligent Data Analysis

Volumn 4, Issue 2, 2000, Pages 165-179

  Mertens, Bart J A ^a   Hand, David J ^a  

^a UNIVERSITY OF AMSTERDAM   (Netherlands)

Author keywords

automated data analysis; classification and regression; combining information; model uncertainty; pattern recognition

Indexed keywords

BAYESIAN NETWORKS; PATTERN RECOGNITION; UNCERTAINTY ANALYSIS;

AUTOMATED DATA ANALYSIS; BAYESIAN MODEL AVERAGING; CLASSIFICATION FRAMEWORK; COMBINATION OF CLASSIFIERS; COMBINING INFORMATION; GENERALIZED ADDITIVE MODEL; MODEL UNCERTAINTIES; RESEARCH AND DEVELOPMENT;

CLASSIFICATION (OF INFORMATION);

EID: 58149342007     PISSN: 1088467X     EISSN: 15714128     Source Type: Journal    
DOI: 10.3233/ida-2000-4206     Document Type: Article

References (27)

1. L. Breiman, Stacked regression. Technical report No. 367, August 1992, Department of Statistics, University of California, Berkeley, California 94720, 1992.
2. L. Breiman, Bagging predictors.Technical report No. 421, August 1994, Department of Statistics, University of California, Berkeley, California 94720, 1994.
3. L. Breiman and J.H. Friedman, Estimating optimal transformations for multiple regression and correlation, J. Am. Statist. Ass. 80 (1985), 580-619.
4. A. Buja, Remarks on functional canonical variates, alternating least squares methods and ACE, The Annals of Statistics 18 (1990), 1032-1069.
5. A. Buja, T. Hastie and R. Tibshirani, Linear smoothers and additive models, The Annals of Statistics 17 (1989), 453-555.
6. W. Buntine, Learning in classification trees, Statistics and Computing 2 (1992), 63-73.
7. L.A. Clark and D. Pregibon, Tree-based models, in: Statistical Models in S, (Chapter 9), J.M. Chambers and T.J. Hastie, eds., Pacific Grove, CA: Wadsworth and Brooks/Cole, 1992.
8. D.J. Hand, Construction and Assessment of Classification Rules, Chichester: Wiley, 1997.
9. P.A. Devijver and J.V. Kittler, Pattern Recognition. A Statistical Approach, Englewood Cliffs: Prentice-Hall, 1982.
10. Y. Freund, Boosting a weak learning algorithm by majority, Information and Computation 121 (1995), 256-285.
11. J.H. Friedman, Regularized discriminant analysis, J. Am. Statist. Ass. 84 (1989), 165-175.
12. J.H. Friedman, Multivariate adaptive regression splines, The Annals of Statistics 19 (1991), 1-141.
13. J.H. Friedman, T. Hastie and R. Tibshirani, Additive Logistic Regression: A statistical view of boosting. Technical report. August 1998, Department of Statistics, Stanford University, 1998.
14. T. Hastie and D. Pregibon, Shrinking trees. Technical report, AT&T Bell Laboratories, 600 Mountain Avenue, Murray Hill, NJ 07947, 1990.
15. T.J. Hastie and R.J. Tibshirani, Generalized Additive Models, London: Chapman and Hall, 1990.
16. D. Firth, Generalized Linear Models, in: Statistical Theory and Modelling. In honour of Sir David Cox, FRS, D.V. Hinkley, N. Reid and E.J. Snell, eds., London: Chapman and Hall, 1991, pp. 60-64.
17. J.T. Key, L.R. Pericchi and A.F.M. Smith, Bayesian model choice: what and why? in: Bayesian Statistics 6. Proceedings of the Sixth Valencia International Meeting, J.M. Bernardo, J.O. Berger, A.P. Dawid and A.F.M. Smith, eds., Oxford: Clarendon Press, 1998, pp. 343-370.
18. D. Madigan and A.E. Raftery, Model selection and accounting for model uncertainty in graphical models using Occam's window, J. Am. Statist. Ass. 89 (1994), 1535-1545.
19. P. McCullagh and J.A. Nelder, FRS, Generalized Linear Models. Second Edition, London: Chapman and Hall, 1989.
20. J.J. Oliver and D.J. Hand, Averaging over decision trees, Journal of Classification (1996).
21. J.R. Quinlan, Learning with continuous classes, in: Proceedings of the Fifth Australian Joint Conference on Applied Artificial Intelligence, A. Adams and L. Sterling, eds., Singapore: World Scientific, 1992, pp. 343-348.
22. B.D. Ripley, Statistical aspects of neural networks, in Networks and Chaos-Statistical and Probabilistic Aspects, O.E. Barndorff-Nielsen, J.L. Jensen and W.S. Kendall, eds., London: Chapman and Hall, 1993, pp. 40-123.
23. B.D. Ripley, Neural networks and related methods for classification, J. R. Statist. Soc. B 56 (1994), 409-456.
24. R.E. Schapire, The strength of weak learnability, Machine Learning 5 (1990), 197-227.
25. S-PLUS 2000 Professional Release 2. Copyright-c 1988-1999 MathSoft, Inc.
26. J.W. Tukey, Data-based graphics: visual display in the decades to come, Statistical Science 5 (1990), 327-339.
27. W.N. Venables and B.D. Ripley, Modern Applied Statistics with S-PLUS, New York: Springer-Verlag, 1994.