Online Ensemble Learning: An Empirical Study

Machine Learning

Volumn 53, Issue 1-2, 2003, Pages 71-109

  Fern, Alan ^a   Givan, Robert ^a  

^a Purdue University   (United States)

Author keywords

Bagging; Boosting; Branch prediction; Decision trees; Ensemble learning; Online learning

Indexed keywords

ALGORITHMS; BENCHMARKING; COMPUTER ARCHITECTURE; MICROPROCESSOR CHIPS; TREES (MATHEMATICS);

ENSEMBLE LEARNING;

LEARNING SYSTEMS;

EID: 0141921552     PISSN: 08856125     EISSN: None     Source Type: Journal    
DOI: 10.1023/A:1025619426553     Document Type: Article

References (34)

1. Bauer, E., & Kohavi, R. (1999). An empirical comparison of voting classification algorithms: Bagging, boosting and variants. Machine Learning, 36:1/2, 105-142.
2. Breiman, L. (1996a). Bagging predictors. Machine Learning, 24:2, 123-140.
3. Breiman, L. (1996b). Arcing classifiers. Technical Report 460, Department of Statistics, University of California, Berkeley, CA.
4. Burger, D., & Austin, T. (1997). The SimpleScalar tool set, version 2.0. Technical Report 1342, Department of Computer Science, University of Wisconsin-Madison.
5. Calder, B., Grunwald, D., Lindsay, D., Jones, M., Martin, J., Mozer, M., & Zorn, B. (1997). Evidence-based static branch prediction using machine learning. ACM Transactions on Programming Languages and Systems, 19:1, 188-222.
6. Chang, P.-Y., Hao, E., & Patt, Y. (1995). Alternative implementations of hybrid branch predictors. In Proceedings of the 28th ACM/ IEEE International Symposium on Microarchitecture (pp. 252-257).
7. Cormen, T. H., Leiserson, C. E., & Rivest, R. L. (1997). Arithmetic circuits. In Introduction to Algorithms. Cambridge, MA: MIT Press.
8. Dietterich, T. G. (2000). An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting, and randomization. Machine Learning, 40:2, 139-158.
9. Domingo, C., & Watanabe, O. (2000). MadaBoost: A modification of AdaBoost. In Proceedings of the Thirteenth Annual Conference on Computational Learning Theory (pp. 180-189).
10. Eden, A., & Mudge, T. (1998). The YAGS branch predictor. In 31th Annual IEEE/ACM Symposium on Microarchitecture (pp. 69-77).
11. Emer, J., & Gloy, N. (1997). A language for describing predictors and its application to automatic synthesis. In Proceedings of the 24th International Symposium on Computer Architecture (pp. 304-314).
12. Fan, W., Stolfo, S., & Zhang, J. (1999). The application of AdaBoost for distributed, scalable and on-line learning. In Proceedings of the Fifth SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 362-366).
13. Fern, A., Givan, R., Falsafi, B., & Vijaykumar, T.N. (2000). Dynamic feature selection for hardware prediction. Technical Report TR-ECE 00-12, School of Electrical & Computer Engineering, Purdue University.
14. Freund, Y. (1995). Boosting a weak learning algorithm by majority. Information and Computation, 121:2, 256-285.
15. Freund, Y., & Schapire, R.E. (1996). Experiments with a new boosting algorithm. In Proceedings of the Thirteenth International Conference on Machine Learning (pp. 148-156).
16. Freund, Y., & Schapire, R.E. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55:1, 119-139.
17. Grove, A., & Schuurmans, D. (1998). Boosting in the limit: Maximizing the margin of learned ensembles. In Proceedings of the Fifteenth National Conference on Artificial Intelligence (pp. 692-699).
18. Gwennap, L. (1996). Digital 21264 sets new standard. In Microprocessor Report, Oct. (pp. 9-15).
19. Heil, T., Smith, Z., & Smith, J. (1999). Improving branch predictors by correlation on data values. In Proceedings of the 32nd Annual International Symposium on Microarchitecutre (pp. 28-37).
20. Jimenez, D., Hanson, H., & Lin, C. (2001). Boolean formula-based branch prediction for future technologies. In Proceedings of the International Conference on Parallel Architectures and Compilation Technologies (pp. 97-106).
21. Jimenez, D., & Lin, C. (2001). Dynamic branch prediction with perceptrons. In Proceedings of the 7th International Symposium on High Performance Computer Architecture (pp. 197-206).
22. Matheus, C.J., & Rendell, L.A. (1989). Constructive induction on decision trees. In Proceedings of the Eleventh International Joint Conference on Artificial Intelligence (pp. 645-650).
23. McFarling, S. (1993). Combining branch predictors. WRL Technical Note TN-36.
24. Merz, C. J., & Murphy, P. M. (1996). UCI repository of machine learning databases. http://www.ics.uci.edu/̃mlearn/MLRepository.html
25. Nair, R. (1995). Dynamic path-based branch correlation. In 28th International Symposium on Microarchitecture (pp. 15-23).
26. Quinlan, J. R. (1986). Induction of decision trees. Machine Learning, 1, 81-106.
27. Quinlan, J. R. (1988). An empirical comparison of genetic and decision-tree classifiers. In Proceedings of the Fifth International Conference on Machine Learning (135-141).
28. Quinlan, J. R. (1996). Bagging, boosting and C4.5. In Proceedings of the Thirteenth National Conference on Artificial Intelligence (pp. 725-730).
29. Reilly, J. (1995). SPEC describes SPEC95 products and benchmarks. Standard Performance Evaluation Corporation August 1995 newsletter, http://www.spec.org.
30. Schlimmer, J. C., & Fisher, D. (1986). A case study of incremental concept induction. In Proceedings of the Fifth National Conference on Artificial Intelligence (pp. 496-501).
31. Schapire, R. E. (1990). The strength of weak learnability. Machine Learning, 5:2, 197-227.
32. Sherwood, T., & Calder, B. (2001). Automated design of finite state machine predictors for customized processors. In Proceedings of the 28th International Symposium on Computer Architecture (pp. 86-97).
33. Utgoff, P. E. (1989). Incremental induction of decision trees. Machine Learning, 4:2, 161-186.
34. Utgoff, P. E., Berkman, N. C., & Clouse, J. A. (1997). Decision tree induction based on efficient tree restructuring. Machine Learning, 29:1, 5-44.