Active learning using adaptive resampling

Subscribe (Full Service)


	Search: The ACM Digital Library The Guide

	Feedback

Active learning using adaptive resampling

Full text

Pdf (138 KB)

Source

International Conference on Knowledge Discovery and Data Mining archive
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining table of contents

Boston, Massachusetts, United States

Pages: 91 - 98

Year of Publication: 2000

ISBN:1-58113-233-6

Authors

Vijay S. Iyengar	IBM Research Division, T.J. Watson Research Center, P.O. Box 218, Yorktown Heights, NY
Chidanand Apte	IBM Research Division, T.J. Watson Research Center, P.O. Box 218, Yorktown Heights, NY
Tong Zhang	IBM Research Division, T.J. Watson Research Center, P.O. Box 218, Yorktown Heights, NY

Sponsors

SIGKDD: ACM Special Interest Group on Knowledge Discovery in Data
AAAI : Am Assoc for Artifical Intelligence
SIGART: ACM Special Interest Group on Artificial Intelligence
SIGMOD: ACM Special Interest Group on Management of Data

Publisher

ACM New York, NY, USA

Bibliometrics

Downloads (6 Weeks): 13, Downloads (12 Months): 89, Citation Count: 16

Additional Information:

references cited by index terms collaborative colleagues

Tools and Actions:	Request Permissions Review this Article Save this Article to a Binder Display Formats: BibTeX EndNote ACM Ref

DOI Bookmark:	Use this link to bookmark this Article: http://doi.acm.org/10.1145/347090.347110 What is a DOI?

REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

	1	Naoki Abe , Hiroshi Mamitsuka, Query Learning Strategies Using Boosting and Bagging, Proceedings of the Fifteenth International Conference on Machine Learning, p.1-9, July 24-27, 1998
	2	Dana Angluin, Queries and Concept Learning, Machine Learning, v.2 n.4, p.319-342, April 1988 [doi>10.1023/A:1022821128753]
	3	Chidanand Apté , Fred Damerau , Sholom M. Weiss, Automated learning of decision rules for text categorization, ACM Transactions on Information Systems (TOIS), v.12 n.3, p.233-251, July 1994 [doi>10.1145/183422.183423]
	4	Eric Bauer , Ron Kohavi, An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants, Machine Learning, v.36 n.1-2, p.105-139, July-August 1999 [doi>10.1023/A:1007515423169]
	5	Michael J. Berry , Gordon Linoff, Data Mining Techniques: For Marketing, Sales, and Customer Support, John Wiley & Sons, Inc., New York, NY, 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	Les Atlas , David Cohn , Richard Ladner , M. A. El-Sharkawi , R. J. Marks, II, Training connectionist networks with queries and selective sampling, Advances in neural information processing systems 2, Morgan Kaufmann Publishers Inc., San Francisco, CA, 1990
	10	David Cohn , Les Atlas , Richard Ladner, Improving Generalization with Active Learning, Machine Learning, v.15 n.2, p.201-221, May 1994 [doi>10.1023/A:1022673506211]
	11	D. Cohn, Z. Ghahramani, and M. Jordan. Active learning with statistical models. Journal of Artificial Intelligence 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	Thomas G. Dietterich, An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, Boosting, and Randomization, Machine Learning, v.40 n.2, p.139-157, Aug. 2000 [doi>10.1023/A:1007607513941]
	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	Yoav Freund , H. Sebastian Seung , Eli Shamir , Naftali Tishby, Information, Prediction, and Query by Committee, Advances in Neural Information Processing Systems 5, [NIPS Conference], p.483-490, November 30-December 03, 1992
	17	Yoav Freund , H. Sebastian Seung , Eli Shamir , Naftali Tishby, Selective Sampling Using the Query by Committee Algorithm, Machine Learning, v.28 n.2-3, p.133-168, Aug./Sept. 1997 [doi>10.1023/A:1007330508534]
	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	Nicholas Kushmerick, Learning to remove Internet advertisements, Proceedings of the third annual conference on Autonomous Agents, p.175-181, April 1999, Seattle, Washington, United States [doi>10.1145/301136.301186]
	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 onMachine Learning, pages 148-156, 1994.
	22	David D. Lewis , William A. Gale, A sequential algorithm for training text classifiers, Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval, p.3-12, July 03-06, 1994, Dublin, Ireland
	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	Andrew McCallum , Kamal Nigam, Employing EM and Pool-Based Active Learning for Text Classification, Proceedings of the Fifteenth International Conference on Machine Learning, p.350-358, July 24-27, 1998
	25	Foster Provost , David Jensen , Tim Oates, Efficient progressive sampling, Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining, p.23-32, August 15-18, 1999, San Diego, California, United States [doi>10.1145/312129.312188]
	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. S. Seung , M. Opper , H. Sompolinsky, Query by committee, Proceedings of the fifth annual workshop on Computational learning theory, p.287-294, July 27-29, 1992, Pittsburgh, Pennsylvania, United States [doi>10.1145/130385.130417]
	28	Sholom M. Weiss , Chidanand Apte , Fred J. Damerau , David E. Johnson , Frank J. Oles , Thilo Goetz , Thomas Hampp, Maximizing Text-Mining Performance, IEEE Intelligent Systems, v.14 n.4, p.63-69, July 1999 [doi>10.1109/5254.784086]
	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	Yiming Yang , Jan O. Pedersen, A Comparative Study on Feature Selection in Text Categorization, Proceedings of the Fourteenth International Conference on Machine Learning, p.412-420, July 08-12, 1997

CITED BY 16

	Sunita Sarawagi , Anuradha Bhamidipaty, Interactive deduplication using active learning, Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, July 23-26, 2002, Edmonton, Alberta, Canada
	Haoran Wu , Tong Heng Phang , Bing Liu , Xiaoli Li, A refinement approach to handling model misfit in text categorization, Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, July 23-26, 2002, Edmonton, Alberta, Canada
	Maytal Saar-Tsechansky , Foster Provost, Active Sampling for Class Probability Estimation and Ranking, Machine Learning, v.54 n.2, p.153-178, February 2004
	C. V. Apte , S. J. Hong , R. Natarajan , E. P. D. Pednault , F. A. Tipu , S. M. Weiss, Data-intensive analytics for predictive modeling, IBM Journal of Research and Development, v.47 n.1, p.17-23, January 2003
	A. Agrawal , J. Basak , V. Jain , R. Kothari , M. Kumar , P. A. Mittal , N. Modani , K. Ravikumar , Y. Sabharwal , R. Sureka, Online marketing research, IBM Journal of Research and Development, v.48 n.5/6, p.671-677, September/November 2004
	Rey-Long Liu , Wan-Jung Lin, Adaptive sampling for thresholding in document filtering and classification, Information Processing and Management: an International Journal, v.41 n.4, p.745-758, July 2005
	Wang Ye , Huang Shang-Teng, Reducing the number of sub-classifiers for pairwise multi-category support vector machines, Pattern Recognition Letters, v.28 n.15, p.2088-2093, November, 2007
	Hinrich Schütze , Emre Velipasaoglu , Jan O. Pedersen, Performance thresholding in practical text classification, Proceedings of the 15th ACM international conference on Information and knowledge management, November 06-11, 2006, Arlington, Virginia, USA
	Rey-Long Liu, Dynamic category profiling for text filtering and classification, Information Processing and Management: an International Journal, v.43 n.1, p.154-168, January 2007
	Aleksander Kołcz , Joshua Alspector, Asymmetric Missing-data Problems: Overcoming the Lack of Negative Data in Preference Ranking, Information Retrieval, v.5 n.1, p.5-40, January 2002
	Rey-Long Liu, Interactive high-quality text classification, Information Processing and Management: an International Journal, v.44 n.3, p.1062-1075, May, 2008
	Foster Provost , Prem Melville , Maytal Saar-Tsechansky, Data acquisition and cost-effective predictive modeling: targeting offers for electronic commerce, Proceedings of the ninth international conference on Electronic commerce, August 19-22, 2007, Minneapolis, MN, USA
	Surong Wang , Manoranjan Dash , Liang-Tien Chia , Min Xu, Efficient sampling of training set in large and noisy multimedia data, ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP), v.3 n.3, p.14-es, August 2007
	Nitesh V. Chawla , Kevin W. Bowyer, Actively exploring creation of face space(s) for improved face recognition, Proceedings of the 22nd national conference on Artificial intelligence, p.809-814, July 22-26, 2007, Vancouver, British Columbia, Canada
	Maytal Saar-Tsechansky , Foster Provost, Active learning for class probability estimation and ranking, Proceedings of the 17th international joint conference on Artificial intelligence, p.911-917, August 04-10, 2001, Seattle, WA, USA
	Erik Boiy , Marie-Francine Moens, A machine learning approach to sentiment analysis in multilingual Web texts, Information Retrieval, v.12 n.5, p.526-558, October 2009