An empirical study of learning from imbalanced data using random forest

Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI

Volumn 2, Issue , 2007, Pages 310-317

  Khoshgoftaar, Taghi M ^a   Golawala, Moiz ^a   Van Hulse, Jason ^a  

^a Florida Atlantic Univ   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; CYBERNETICS; RANDOM PROCESSES;

EMPIRICAL EVALUATIONS; EMPIRICAL STUDIES; IMBALANCED DATA; INTERNATIONAL CONFERENCES; RANDOM FORESTS;

KNOWLEDGE BASED SYSTEMS;

EID: 48649089002     PISSN: 10823409     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICTAI.2007.46     Document Type: Conference Paper

References (24)

1. D. W. Aha. Lazy learning. Kluwer Academic Publishers, Norwell, MA, USA, 1997.
2. M. L. Berenson, D. M. Levine, and M. Goldstein. Intermediate Statistical Methods and Applications: A Computer Package Approach. Prentice-Hall, Inc., 1983.
3. C. Blake and C. Merz. UCI repository of machine learning databases. http://www.ics.uci.edu/ mlearn/ MLRepository.html, 1998. Department of Information and Computer Sciences, University of California, Irvine.
4. P. Boinee, A. D. Angelis, and G. L. Foresti. Ensembling classifiers - an application to image data classification from cherenkov telescope experiment. In IEC (Prague), pages 394-398, 2005.
5. L. Breiman. Bagging predictors. Machine Learning, 26(2):123-140, 1996.
6. L. Breiman. Random forests. Machine Learning, 45(1):5-32, 2001.
7. C. Chen, A. Liaw, and L. Breiman. Using random forest to learn imbalanced data. Technical Report 666, Statistics Department, University of California at Berkeley, Available at http://www.stat.berkeley.edu/users/chenchao/666.pdf, 2004.
8. P. Domingos and M. Pazzani. On the optimality of the simple bayesian classifier under zero-one loss. Machine Learning, 2-3(29):103-130, 1997.
9. J. D. E. Altendrof, P. Brende and L. Lessard. Fraud detection for online retail using random forests. Technical Report, 2005.
10. Y. Freund and R. Schapire. Experiments with a new boosting algorithm. In Proceedings of the Thirteenth International Conference on Machine Learning, pages 148-156, 1996.
11. D. J. Hand. Good practice in retail credit scorecard assessment. Journal of the Operational Research Society, 56:1109-1117, 2005.
12. T. K. Ho. The random subspace method for constructing decision forests. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(8):832-844, 1998.
13. F. Livingston. Implementation of breiman's random forest machine learning algorithm. Machine Learning Journal Paper, Fall 2005.
14. Y. Ma, L. Guo, and B. Cukic. A statistical framework for the prediction of fault-proneness. Advances in Machine Learning Applciations in Software Engineering, pages 237-263, 2007.
15. T. M. Mitchell. Machine Learning. McGraw-Hill, New York, NY, 1997.
16. F. Provost and T. Fawcett. Robust classification for imprecise environments. Machine Learning, 42:203-231, 2001.
17. J. R. Quinlan. Induction of decision trees. Machine Learning, 1(1):81-106, 1986.
18. J. R. Quinlan. C4.5: Programs For Machine Learning. Morgan Kaufmann, San Mateo, California, 1993.
19. SAS Institute. SAS/STAT User's Guide. SAS Institute Inc., 2004.
20. B. Schlkopf, C. J. C. Burges, and A. J. Smola, editors. Advances in Kernel Methods: Support Vector Learning. MIT Press, Cambridge, Massachusetts, 1999.
21. G. M. Weiss and F. Provost. Learning when training data are costly: the effect of class distribution on tree induction. Journal of Artificial Intelligence Research, (19):315-354, 2003.
22. I. H. Witten and E. Frank. Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann, San Francisco, California, 2nd edition, 2005.
23. C. Wohlin, P. Runeson, M. Host, M. C. Ohlsson, B. Regnell, and A. Wesslen. Experimentation in Software Engineering: An Introduction. Kluwer Academic Publishers, Boston, MA, 2000.
24. J. Zhang and M. Zulkernine. Network intrusion detection using random forests. Proc. Of the Third Annual Conference on Privacy, Security and Trust (PST), pages 53-61, October 2005.