Asymmetric missing-data problems: Overcoming the lack of negative data in preference ranking

Information Retrieval

Volumn 5, Issue 1, 2002, Pages 5-40

  Kołcz, Aleksander ^a   Alspector, Joshua ^b  

^a Personalogy Inc   (United States)

^b UNIVERSITY OF COLORADO   (United States)

Author keywords

Imbalanced training data; Incomplete data problems; Information retrieval; Personalization; User modeling

Indexed keywords

EID: 11244278806     PISSN: 13864564     EISSN: None     Source Type: Journal    
DOI: 10.1023/A:1012714523368     Document Type: Article

References (51)

1. Billsus D and Pazzani M (1999) A hybrid user model for news story classification. In: Seventh International Conference on User Modeling (UM '99). http://www.ics.uci.edu/~pazzani/Publications/um99.ps.
2. Blum A and Mitchell T (1998) Combining labeled and unlabeled data with co-training. In: Proceedings of the 1998 Conference on Computational Learning Theory.
3. Buckley C and Salton G (1995) Optimization of relevance feedback weights In: Proceedings of 18th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 351-357.
4. Buckley C, Salton G, Allan J and Singhal A (1995) Automatic query expansion using SMART. TREC-3. In: Harman DK. Ed., Proceedings of the 3rd Text Retrieval Conference (TREC-3) (NIST SP 500-225).
5. Claypool M, Gokhale A, Miranda T, Murnikov P, Netes D and Sartin M (1999) Combining contentbased and collaborative filters in an online newspaper. ACM SIGIR Recommander Systems Workshop. http://www.cs.wpi.edu/~claypool/papers/content-collab/content-collab.ps.
6. Cohn D, Atlas L and Ladner R (1994) Improving generalization with active learning. Machine Learning, 15(2):201-221.
7. Croft W and Harper D (1979) Using probabilistic models of document retrieval without relevance information. Journal of Documentation, 35:285-295.
8. Dempster AP, Laird NM and Rubin DB (1977) Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society. Series B, 39.1-38.
9. Domingos P (1999) MetaCost. A general method for making classifiers cost-sensitive. In: Proceedings of the Fifth International Conference on Knowledge Discovery and Data Mining (KDD-99).
10. Foltz PW and Dumais ST (1992) Personalized information delivery An analysis of information filtering methods. Communications of the ACM, 35(12):51-60. http://www-psychnmsuedu/~pfoltz/cacm/cacm.html.
11. Frakes WB and Baeza-Yates R (1992) Eds., Information Retrieval: Data Structures and Algorithms. Prentice-Hall. Boston. MA.
12. Harman D (1992) Ranking algorithms. In: Frakes WB and Baeza-Yates R, Eds., Information Retrieval. Data Structures and Algorithms Prentice-Hall, Boston, MA. pp. 363-392.
13. Iyengar V, Apte C and Zhang T (2000) Active learning using adaptive resampling. In: Proceedings of ACM SIGKDD 2000.
14. Japkowicz N (2000) The class imbalance problem: Significance and strategies. In: Proceedings of the 2000 International Conference on Artificial Intelligence, http://www.csdalca/~nat/Papers/ic-ai-2000.ps.
15. Jennings A, Higichi H and Liu H (1993) A user model neural network for a personal news service. Australian Telecommunication Research, 27(1): 1-12.
16. Joachims T (1997) Text categorization with support vector machines: Learning with many relevant features. Technical Report LS-8/23, University of Dortmund.
17. Joachims T (1999) Making large-scale svm learning practical. In: Schoelkopf B, Burges C and Smola A, Eds.. Advances in Kernel Methods - Support Vector Learning. MIT Press.
18. Joachims T. Freitag D and Mitchell T (1997) Webwatcher: A tour guide for the worldwide web In: Proceedings of the International Joint Conference on Artificial Intelligence, http://www.es.cmu.edu/afs/cs/pioject/theo-6/web-agent/www/ijcat97.ps
19. Kamba T. Sahagami H and Koseki Y (1997) ANATANAGONOMY: A personalized newspaper on the world wide web. Int J. Human-Compuer Studies, 46:789-803
20. Kubat M, Holte R and Matwin S (1997) Learning when negative examples abound. In: Proceedings of the European Conference on Machine Learning, ECML'97, pp. 146-153. http://www.cacs.louisiana.edu/~ mkubat/publications/imbalanced.ps.
21. Kubat M and Matwin S (1997) Addressing the curse of imbalanced training sets: One-sided selection. In: Proceedings of the 14th International Conterence on Machine Learning, ICML'97. pp. 179-186. http://www.cacs louisiana.edu/~mkubat/publications/sampling.ps.
22. Lang K (1995) News Weeder: Learning to filter NetNews. In Proceedings of the 12th International Conference on Machine Learning: ICML-95, pp. 331-339.
23. Lewis DD (1998) Naive (bayes) at forty: The independence assumption in information retrieval. In: Proceedings of the 10th European Conference on Machine Learning, pp. 4-15. http://www.research.att.comm/ ~lewis/papers/lewis98bps.
24. Lewis DD and Gale WA (1994) A sequential algorithm for training text classifiers. In. Croft WB and van Rijsbergen CJ, Eds., SIGIR 94: Proceedings of the Seventeenth Annual International ACM-SIGIR Conference on Research and Development in Information Retrieval. Springer-Verlag, Dublin, Ireland, pp. 3-12.
25. Lieberman H (1995) Letizia: An agent that assists web browsing. In: Proceedings of the International Joint Conference on Artificial Intelligence, pp. 924-929. http://lieber.www.mediamit.edu/people/lieber/Lieberary/ Letizia/Letizia-AAAI/Letizia.html.
26. Losee RM (1998) Text Retrieval & Filtering. Analytic Models of Performance. Kluwer Academic Publishers. New York.
27. McCallum AK and Nigam K (1997) Employing EM in pool-based active learning for text classification. In: Proceedings of the 1998 International Machine Learning Conference, pp 25-32. http//www.cs cmu.edu/~mccallum/papers/emactive-icm 198.ps.gz.
28. McCallum AK and Nigam K (1998) A comparison of event models for naive bayes text classification. AAAI-98 Workshop on Learning for Text Categorization http://www.cscmu.edu/~mccallum/papers/multinomialaaai98w.ps
29. McLachlan GJ and Krishnan T (1996) The EM Algorithm and Extensions. John Wiley & Sons, Philadelphia. PA
30. Mitra M, Singhal A and Buckley C (1997) Learning queries in a query zone. In: Proceedings of the 20th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 25-32.
31. Morik K. Imboff M, Brockhausen P, Joachims T and Gather U (2000) Knowledge discovery and knowledge validation in intensive care. Artificial Intelligence in Medicine, 19(3):225-249.
32. Morita M and Shinoda Y (1994) Information filtering based on user behavior analysis and best match text retrieval. In: Pioceedings of 17th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 272-281.
33. Nickerson A, Japkowicz N and Milios E (2001) Using unsupervised learning to guide resampling in imbalanced data sets. In: Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics.
34. Nigam K and Ghani R (2000) Analyzing the effectiveness and applicability of co-training In: Proceedings of the Ninth International Conference on Information and Knowledge Management.
35. Nigam K, McCallum AK, Thrun S and Mitchell T (2000) Text classification from labeled and unlabeled documents using EM. Machine Learning, 39(2):103-134.
36. Pazzani M and Billsus D (1997) Learning and revising user profiles: The identification of interesting web sites. Machine Learning, 27:313-331. http://www.ics.uci,edu/~pazzani/publications/SW-MLJ.pdf
37. Pazzani M, Merz C, Murphy. P. Ali K, Hume T and Brunk C (1994) Reducing misclassification costs. In: 11th International Conference of Machine Learning, pp. 217-225. http-//wwwicsuci.edu/~pazzani/publications/ MLC94.pdf.
38. Pednault E, Rosen BK and Apte C (2000) Handling imbalanced data sets in insurance risk modeling. Technical Report RC-21731, IBM.
39. Platt J (2000) Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. In Smola A. Bartlett P, Scholkopf B and Schuurmans D, Eds., Advances in Large-Margin Classifiers (Neural Information Processing), MIT Press.
40. Porter M (1980) An algorithm for suffix stripping Program (Automated Library and Information Systems ), 14(3):130-137.
41. Provost F (2000) Machine learning from imbalanced data sets 101. In: Japkowicz N, Ed Learning from Imbalanced Data Sets - Papers from the AAAI Workshop. AAAI Press, Austin, TX, pp. 1-3.
42. Robertson SE and Sparck-Jones K (1976) Relevance weighting of search terms JASIS pp. 129-176.
43. Rocchio J (1971) Relevance feedback in information retrieval. In: Salton G, Ed.. The SMART Retrieval System: Experiments in Automatic Document Processing. Prentice-Hall, pp. 313-323.
44. Salton G (1971) The SMART Retrieval System: Experiments in Automatic Document Processing Prentice-Hall, Melbourne, Australia.
45. Schapire RE, Singer Y and Singhal, A (1998) Boosting and Rocchio applied to text filtering. In: Proceedings of 21st International ACM SIGIR Conference on Research and Development in Information Retrieval, pp 215-223. http://www.researchatt.com/~schapire/cgi-bin/uncoinpress-papers/SchapireSiSi98. ps.
46. Schohn G and Cohn D (2000) Less is more: Active learning with support vector machines. In: Proceedings of the Seventeenth International Conference on Machine Learning.
47. Seung HS, Opper M and Sompolinsky H (1992) Query by committee. In: Proceedings of the Fifth Annual ACM Workshop on Computational Learning Theory, pp. 287-294.
48. Stevens C (1992) Automating the creation of information filters. Communications of the ACM, 35(12).48.
49. van Rijsbergen CJ (1979) Information Retrieval. Buttersworth, London.
50. Vapnik VN (1998) Statistical Learning Theory. John Wiley, New York.
51. Yang Y and Liu X (1999) A re-examination of text categorization methods. In: Proceedings of the 22nd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 42-49. http://www.cs.cmu.edu/-~yiming/papersyy/sigir99.ps.