Adaptive sampling methods for scaling up knowledge discovery algorithms

Data Mining and Knowledge Discovery

Volumn 6, Issue 2, 2002, Pages 131-152

  Domingo, Carlos ^a   Gavaldà, Ricard ^b   Watanabe, Osamu ^a  

^a TOKYO INSTITUTE OF TECHNOLOGY   (Japan)

^b UNIVERSITAT POLITÈCNICA DE CATALUNYA   (Spain)

Author keywords

Adaptive sampling; Concentration bounds; Data mining; Knowledge discovery; Scalability

Indexed keywords

ALGORITHMS; APPROXIMATION THEORY; DATA MINING; KNOWLEDGE ENGINEERING;

KNOWLEDGE DISCOVERY ALGORITHMS;

ADAPTIVE SYSTEMS;

EID: 0036102015     PISSN: 13845810     EISSN: None     Source Type: Journal    
DOI: 10.1023/A:1014091514039     Document Type: Article

References (21)

1. Bauer, E. and Kohavi, R. 1998. An empirical comparison of voting classification algorithms: Bagging, boosting, and variants. Machine Learning, 36:105-139.
2. Domingo, C., Gavaldà, R., and Watanabe, R. 1998. Practical algorithms for on-line selection. In Proceedings of the First International Conference on Discovery Science (DS'98), Lecture Notes in Artificial Intelligence, vol. 1532. (pp. 150-161).
3. Domingo, C., Gavaldà, R., and Watanabe, O. 1999. On-line sampling methods for discovering association rules. Dept. of Math and Computing Science, Tokyo Institute of Technology, Tokyo Tech Rep. C-126.
4. Domingo, C. and Watanabe, O. 1999. MadaBoost: A modification of AdaBoost for the filtering framework. Dept. of Math and Computing Science, Tokyo Institute of Technology, Tokyo, Tech Rep. C-138. Available at www.is.titech.ac.jp/research/research-report/C/index.html. Also submitted for publication.
5. Domingo, C. and Watanabe, O. 2000a. Scaling-up a boosting based learner via Adaptive Sampling. In Proceedings of the Fourth Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'2000), to appear.
6. Domingo, C. and Watanabe, O. 2000b. Experimental evaluation of an adaptive boosting by filtering algorithm. Submitted for publication. Dept. of Math and Comp. Science, Tokyo Insitute of Technology, Tokyo, Tech Rep. C-139. Available at www.is.titech.ac.jp/research/research-report/C/index.html.
7. Dougherty, J., Kohavi, R., and Sahami, M. 1995. Supervised and unsupervised discretization of continuous features. In Proceedings of the Twelfth International Conference on Machine Learning.
8. Fayad, U.M. and Irani, K.B. 1993. Multi-interval discretization of continuous-valued attributes for classification learning. In Proceedings of the 13th International Joint Conference on Artificial Intelligence, pp. 1022-1027.
9. Feller, W. 1950. An Introduction to Probability Theory and its Applications. New York: John Willey and Sons.
10. Freund, Y. and 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.
11. John, G.H. and Langley, P. 1996. Static versus dynamic sampling for data mining. In Proc. of the Second International Conference on Knowledge Discovery and Data Mining. AAAI/MIT Press.
12. Kearns, M.J. and Vazirani, U. 1994. An Introduction to Computational Learning Theory. Cambridge: Cambridge University Press.
13. Keogh, E., Blake, C. and Merz, C.J. 1998. UCI repository of machine learning databases. Available at http://www.ics.uci.edu/mlearn/MLRepository.html. Irvine, CA: University of California, Department of Information and Computer Science.
14. Kivinen, J. and Mannila, H. 1994. The power of sampling in knowledge discovery. In Proceedings of the ACM SIGACT-SIGMOD-SIGACT Symposium on Principles of Database Theory, pp. 77-85.
15. Lipton, R.J. and Naughton, J.F. 1995. Query size estimation by adaptive sampling. Journal of Computer and System Science, 51:18-25.
16. Lipton, R.J., Naughton, J.F., Schneider, D.A. and Seshadri, S. 1995. Efficient sampling strategies for relational database operations. Theoretical Computer Science, 116:195-226.
17. Provost, F., Jensen, D., and Oates, T. 1999. Efficient progressive sampling. In Proceedings of the 5th International Conference on Knowledge Discovery and Data Mining.
18. Quinlan, J.R. 1993. C4.5: Programs for machine learning. San Mateo, CA: Morgan Kaufmann.
19. Toivonen, H. 1996. Sampling large databases for association rules. In Proceedings of the 22nd International Conference on Very Large Databases, pp. 134-145.
20. Wald, A. 1947. Sequential Analysis. Wiley Mathematical, Statistics Series. New York: Wiley.
21. Wang, M., Iyer, B., and Vitter, J.S., 1998. MIND: A scalable mining for classifier in relational databases. In Proceedings of the ACM SIGMOD Workshop on Research Issues on Data Mining and Knowledge Discovery (DMKD).