An introduction to ensemble methods for data analysis

Sociological Methods and Research

Volumn 34, Issue 3, 2006, Pages 263-295

  Berk, Richard A ^a,b,c,d  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b AMERICAN STATISTICAL ASSOCIATION   (United States)

^c AAAS   (United States)

^d Academy of Experimental Criminology   (United States)

Author keywords

Algorithmic methods; CART; Data analysis; Ensemble methods

Indexed keywords

EID: 31944452324     PISSN: 00491241     EISSN: 15528294     Source Type: Journal    
DOI: 10.1177/0049124105283119     Document Type: Article

References (31)

1. Berk, R. A., and J. Back. 2003. "Ensemble Procedures for Finding High Risk Prison Inmates." Unpublished manuscript, Department of Statistics, UCLA.
2. Berk, R. A., A. Li, and L. J. Hickman. Forthcoming. "Statistical Difficulties in Determining the Role of Race in Capital Cases: A Re-analysis of Data From the State of Maryland." Quantitative Criminology.
3. Berk, R. A., S. B. Sorenson, and Y. He. 2005. "Developing a Practical Forecasting Screener for Domestic Violence Incidents." Evaluation Review 29:358-382.
4. Breiman, L. 1996. "Bagging Predictors." Machine Learning 26:123-40.
5. _. 2001a. "Random Forests." Machine Learning 45:5-32.
6. _. 2001b. "Statistical Modeling: Two Cultures" (with discussion). Statistical Science 16:199-231.
7. _. 2001c. "Wald Lecture I: Machine Learning." ftp://ftp.stat.berkeley.edu/pub/users/breiman/.
8. Breiman, L., J. H. Friedman, R. A. Olshen, and C. J. Stone. 1984. Classification and Regression Trees. Monterey, CA: Wadsworth.
9. Buehlmann, P. and Bin Yu. 2002. "Analyzing Bagging." The Annals of Statistics 30:927-61.
10. Christianini, N. and J. Shawne-Taylor. 2000. An Introduction to Support Vector Machines. Cambridge, UK: Cambridge University Press.
11. Dasu, T. and T. Johnson. 2003. Exploratory Data Mining and Data Cleaning. New York: John Wiley.
12. Faraway, J. 2004. "Human Animation Using Nonparametric Regression." Journal of Computational and Graphical Statistics 13:537-53.
13. Freund, Y. and R. Schapire. 1996. "Experiments With a New Boosting Algorithm." Pp. 148-56 in Machine Learning: Proceedings for the Thirteenth International Conference. San Francisco: Morgan Kaufmann.
14. Friedman, J., T. Hastie, and R. Tibsharini. 2000. "Additive Logistic Regression: A Statistical View of Boosting" (with discussion). Annals of Statistics 28:337-407.
15. Friedman, J. H. 2001. "Greedy Function Approximation: A Gradient Boosting Machine." Annals of Statistics 29:1189-1232.
16. _. 2002. "Stochastic Gradient Boosting." Computational Statistics and Data Analysis 38 (4): 367-78.
17. Grandvalet, Y. 2004. "Bagging Equalizes Influence." Machine Learning 55:251-70.
18. Hastie, T., R. Tibshirani, and J. Friedman. 2001. The Elements of Statistical Learning. New York: Springer-Verlag.
19. Hothorn, T. 2003. "Bundling Predictors in R." DSC 2003 Working Papers (draft version). http://www.ci.tuwien.ac.at/Conferences/DSC-2003/
20. Hothorn, T., L. Berthold, A. Brenner, and M. Radespiel-Tröger. 2002. "Bagging Survival Trees." Preprint, Department of Medical Informatics, Biometry and Epidemiology, Freidrich-Alexander-University Erlangen-Nuremberg.
21. LeBlanc, M. and R. Tibshirani. 1996. "Combining Estimates on Regression and Classification." Journal of the American Statistical Association 91:1641-50.
22. Loh, W.-Y. 2002. "Regression Trees With Unbiased Variable Selection and Interaction Detection." Statistica Sinica 12:361-86.
23. Mannor, S., R. Meir, and T. Zhang. 2002. "The Consistency of Greedy Algorithms for Classification." COLT 2002:319-33.
24. Mertz, C.J. 1999. "Using Correspondence Analysis to Combine Classifiers." Machine Learning 36:33-58.
25. Mojirsheibani, M. 1997. "A Consistent Combine Classification Rule." Statistics & Probability Letters 36:43-7.
26. _. 1999. "Combining Classifiers vis Discretization." Journal of the American Statistical Association 94:600-9.
27. Rosenbaum, P. R. 2002. Observational Studies, 2nd ed. New York: Springer-Verlag.
28. Schapire, R. E. 1999. "A Brief Introduction to Boosting." In Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence.
29. Su, X., M. Wang, and J. Fan. 2004. "Maximum Likelihood Regression Trees." Journal of Computational and Graphical Statistics 13:586-98.
30. Witten, I. H. and E. Frank. 2000. Data Mining. New York: Morgan Kaufman.
31. Zhang, H. and B. Singer. 1999. Recursive Partitioning in the Health Sciences. New York: Springer-Verlag.