Research on AdaBoost.M1 with Random Forest

ICCET 2010 - 2010 International Conference on Computer Engineering and Technology, Proceedings

Volumn 1, Issue , 2010, Pages

  Zhang, Zhenyu ^a   Xie, Xiaoyao ^b  

^a CHINA MOBILE COMMUNICATIONS CORPORATION   (China)

^b GUIZHOU NORMAL UNIVERSITY   (China)

Author keywords

AdaBoost; AdaBoost.M1; AdaBoost.M1 RF; Random Forest

Indexed keywords

ADABOOST; ADABOOST.M1; ADABOOST.M1-RF; MACHINE LEARNING ALGORITHMS; NEW APPROACHES; PREDICTION PERFORMANCE; RANDOM FORESTS; WEAK LEARNER;

ADAPTIVE BOOSTING; ALGORITHMS; DECISION TREES;

LEARNING SYSTEMS;

EID: 77958029769     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICCET.2010.5485910     Document Type: Conference Paper

References (21)

1. Guy Leshem, Improvement of Adaboost Algorithm by using Random Forests as Weak Learner using this algorithm as statistics machine learning for traffic flow prediction, Research proposal for a Ph.D. Thesis, 2005
2. M Kearns, L Valiant, Cryptographic limitations on learning Boolean formulae and finite automata, Journal of the ACM (JACM), 1994
3. Y. Freund and E. R. Schapire. A short introduction to boosting. Journal of Japanese Society for Artificial Intelligence, 14(5):771-780, 1999.
4. Y. Freund and E. R. Schapire. A decision theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55(1):119-139, 1997.
5. Y. Freund and E. R. Schapire. A short introduction to boosting. Journal of Japanese Society for Artificial Intelligence, 14(5):771-780, 1999.
6. Y. Freund, E. R. Schapire, S. W. Lee, and P. Bartlett. Boosting the margin: A new explanation for the effectiveness of voting method. The Annals of Statistics, 26, No. 5:1651-1686, 1998.
7. Y. Freund, R. Schapire, Experiments with a new boosting algorithm In Saitta, L. (Ed), Proceedings of the 13th International Conference on Machine Learning, 148-156. Morgan Kaufmann (1996)
8. E. R. Schapire. The boosting approach to machine learning: An overview, Nonlinear Estimation and Classification. Springer, 2003.
9. R Meir, G Ratsch, An Introduction to Boosting and Leveraging, LECTURE NOTES IN COMPUTER SCIENCE, 2003
10. Jerome Friedman, Trevor Hastie, Robert Tibshirani, ADDITIVE LOGISTIC REGRESSION: A STATISTICAL VIEW OF BOOSTING, The Annals of Statistics 2000, vol. 28, no. 2, 337-407
11. Y Freund. and R.E. Schapire, Game theory, on-line prediction and boosting. In Proceedings of the Ninth Annual Conference on Computational Learning Theory 325-332, 1996
12. L. Breiman, Prediction games and arcing algorithms. Technical Report 504, Dept. Statistics, Univ. California, Berkeley, 1997
13. L. Breiman. Random forests, technical report. Statistics department, University of California, Berkeley, 1999.
14. L. Breiman. Random forests. Machine Learning, 45(1):5-32, 2001.
15. Y. Freund, R. Schapire, Experiments with a new boosting algorithm In Saitta, L. (Ed), Proceedings of the 13th International Conference on Machine Learning, 148-156. Morgan Kaufmann (1996)
16. L. Breiman, Prediction games and arcing algorithms. Technical Report 504, Dept. Statistics, Univ. California, Berkeley, 1997
17. Guy Leshem, Improvement of Adaboost Algorithm by using Random Forests as Weak Learner using this algorithm as statistics machine learning for traffic flow prediction, Research proposal for a Ph.D. Thesis, 2005
18. Guy Leshem, Traffic flow prediction and minimization of traffic congestion using Adaboost-RandomForests algorithm, Phd Dissertation the Hebrew University, 2007
19. P. Boinee et al, Meta Random Forests, In International Journal for Computational Intelligence, Volume-II, pages: 138-147 (2005)
20. P. Boinee, Insights into machine learning: Data clustering and classification algorithms for astrophysical experiments, Phd Dissertation Department of Mathematics and Computer Science University of Udine, Italy, 2006
21. Guo Shan-Qing, Gao Cong et al, An Intrusion Detection Model Based on Improved Random Forests Algorithm, Journal of Software Vol. 16, No.8 1000-9825