Active learning using adaptive resampling

Proceeding of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Volumn , Issue , 2000, Pages 91-98

  Iyengar, Vijay S ^a   Apte, Chidanand ^a   Zhang, Tong ^a  

^a IBM T J WATSON RESEARCH CENTER   (United States)

Author keywords

Active learning; Adaptive resampling; Classification; Data mining; Machine learning

Indexed keywords

ADAPTIVE SYSTEMS; CLASSIFICATION (OF INFORMATION); DATA MINING; MATHEMATICAL MODELS; SAMPLING;

ADAPTIVE RESAMPLING;

LEARNING SYSTEMS;

EID: 0034592915     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/347090.347110     Document Type: Conference Paper

References (31)

1. N. Abe and H. Mamitsuka. Query learning strategies using boosting and bagging. In Proceedings of the International Conference on Machine Learnings pages 1-9, 1998.
2. D. Angluin. Queries and concept learning. Machine Learning, 2(4):319-342, 1988.
3. C. Apte, F. Damerau, and S. Weiss. Automated learning of decision rules for text categorization. ACM Transactions on Information Systems, 12(3):233-251, July 1994.
4. E. Bauer and R. Kohavi. An empirical comparison of voting classification algorithms: Bagging, boosting, and variants. Machine Learning, 36:105-142, 1999.
5. M. Berry and G. Linoff. Data Mining Techniques: For Marketing, Sales, and Customer Support. John Wiley and Sons, Inc., 1997.
6. C. Blake, E. Keogh, and C. Merz. UCI repository of machine learning databases. University of California, Irvine, Dept. of Information and Computer Science, URL=http://www.ics.uci.edu/̃mlearn/-MLRespository.html, 1998.
7. L. Breiman. Arcing classifiers. The Annals of Statistics, 26(3):801-849, 1998.
8. W. Cochran. Sampling Techniques. John Wiley and Sons, Inc., 1977.
9. D. Cohn, L. Atlas, and R.Ladner. Training connectionist networks with queries and selective sampling. In Advances in Neural Information Processing Systems 2. Morgan Kaufmann, 1990.
10. D. Cohn, L. Atlas, and R.Ladner. Improved generalization with active learning. Machine Learning, 15:201-221, 1994.
11. D. Cohn, Z. Ghahramani, and M. Jordan. Active learning with statistical models. Journal of Artificial Intellgence Research, 4:129-145, 1996.
12. D. Cohn, Z. Ghahramani, and M. Jordan. Active learning with mixture models. In Multiple model approaches to modeling and control. Taylor and Francis, 1997.
13. T. G. Dietterich. An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting and randomization. Machine Learning, 40(2), August 2000.
14. Y. Freund. Sifting informative examples from a random source. In Advances in Neural Information Processing, pages 85-89, 1994.
15. Y. Freund and R. Schapire. Experiments with a new boosting algorithm. In Proceedings of the International Conference on Machine Learning, pages 148-156. Morgan Kaufmann, 1996.
16. Y. Freund, H. Seung, E. Shamir, and N. Tishby. Information, prediction, and query by committee. In Advances in Neural Information Processing Systems 5, pages 337-344. Morgan Kaufmann, 1992.
17. Y. Freund, H. Seung, E. Shamir, and N. Tishby. Selective sampling using the query by committee algorithm. Machine Learning, 28:133-168, 1997.
18. J. Friedman, T. Hastie, and R. Tibshirani. Additive logistic regression: A statistical view of boosting. Technical Report Technical Report, Stanford University, Dept. of Statistics, July 1998.
19. N. Kushmerick. Learning to remove internet advertisements. In Proceedings of the Third International Conference on Autonomous Agents, pages 175-181, 1999.
20. D. Lewis. Reuters 21578 data set. URL=http://www.research.att.com/lewis/-reuters21578.html.
21. D. Lewis and J. Catlett. Heterogeneous uncertainty sampling for supervised learning. In Proceedings of the Eleventh International Conference on Machine Learning, pages 148-156, 1994.
22. D. Lewis and W. Gale. A sequential algorithm for training text classifiers. In Proceedings of the Seventeenth Annual A CM-SIGR Conference on Research and Development in Information Retrieval, pages 3-12, 1994.
23. R. Liere and P. Tadepalli. Active learning with committees for text categorization. In Proceedings of the Fourteenth National Conference on Artificial Intelligence, pages 591-596, 1997.
24. A. McCallum and K. Nigam. Employing em in pool-based active learning for text classification. In Proceedings of the Fifteenth International Conference on Machine Learning, pages 350-358, 1998.
25. F. Provost, D. Jensen, and T. Oates. Efficient progressive sampling. In Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data mining, pages 23-32, 1999.
26. R. Schapire, Y. Freund, P. Bartlett, and W. Lee. Boosting the margin: A new explanation for the effectiveness of voting methods. The Annals of Statistics, 26(5):1651-1686, 1998.
27. H. Seung, M. Opper, and H. Sompolinsky. Query by committee. In Proceedings of the Fifth ACM Workshop on Computational Learning Theory, pages 287-294, 1992.
28. S. Weiss, C. Apte, F. Damerau, D. Johnson, F. Oles, T. Goetz, and T. Hampp. Maximizing text-mining performance. IEEE Intelligent Systems and their applications, 14(4):63-69, July/August 1999.
29. S. Weiss and N. Indurkhya. Data-miner software kit (DMSK). URL=http://www.data-miner.com, 1998.
30. S. M. Weiss and C. A. Kulikowski. Computer Systems that Learn. Morgan Kaufmann, 1991.
31. Y. Yang and J. Pedersen. A comparitive study on feature selection in text categorization. In ICML '97, Proceedings of the Fourteenth International Conference on Machine Learning, pages 412-420, 1997.