Boosted decision trees, a powerful event classifier

Statistical Problems in Particle Physics, Astrophysics and Cosmology - Proceedings of PHYSTAT 2005

Volumn , Issue , 2006, Pages 139-142

  Roe, Byron P ^a   Yang, Hai Jun ^a   Zhu, Ji ^a  

^a UNIVERSITY OF MICHIGAN   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COSMOLOGY; EQUIPMENT TESTING;

BOOSTED DECISION TREES; DECISION TREE METHOD; MINIBOONE; SPEED OF CONVERGENCE;

DECISION TREES;

EID: 84873840381     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (7)

1. R.E. Schapire, "The strength of weak learnability", Machine Learning 5 (2), 197-227 (1990). First suggested the boosting approach for 3 trees taking a majority vote.
2. Y. Freund, "Boosting a weak learning algorithm by majority", Information and Computation 121 (2), 256-285 (1995) Introduced using many trees.
3. Y. Freund and R.E. Schapire, "Experiments with an new boosting algorithm", Machine Learning: Proceedings of the Thirteenth International Conference, Morgan Kauffman, SanFrancisco, pp.148-156 (1996). Introduced AdaBoost.
4. J. Friedman, T. Hastie, and R. Tibshirani, "Additive logistic regression: A statistical view of boosting", Annals of Statistics 28 (2), 337-407 (2000). Showed that AdaBoost could be looked at as successive approximations to a maximum likelihood solution.
5. T. Hastie, R. Tibshirani, and J. Friedman, "The Elements of Statistical Learning", Springer (2001). Good reference for decision trees and boosting.
6. B.P. Roe, et al., "Boosted decision trees as an alternative to artificial neural networks for particle identification", NIM A543, pp. 577-584 (2005).
7. Hai-Jun Yang, Byron P. Roe, and Ji Zhu, "Studies of Boosted Decision Trees for MiniBooNE Particle Identification", Physics/0508045, July 2005. Accepted NIM, Sept. 16, 2005.