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Editorial: special issue on learning from imbalanced data sets

Chawla, Nitesh V., Japkowicz, Nathalie, Kotcz, Aleksander·2004·ACM SIGKDD Explorations Newsletter
DOI 10.1145/1007730.1007733
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Abstract

article Share on Editorial: special issue on learning from imbalanced data sets Authors: Nitesh V. Chawla Retail Risk Management, CIBC, Toronto, ON, Canada Retail Risk Management, CIBC, Toronto, ON, CanadaView Profile , Nathalie Japkowicz University of Ottawa, ON, Canada University of Ottawa, ON, CanadaView Profile , Aleksander Kotcz AOL, Inc., Dulles, VA AOL, Inc., Dulles, VAView Profile Authors Info & Claims ACM SIGKDD Explorations NewsletterVolume 6Issue 1June 2004pp 1–6https://doi.org/10.1145/1007730.1007733Published:01 June 2004Publication History 1,584citation10,437DownloadsMetricsTotal Citations1,584Total Downloads10,437Last 12 Months454Last 6 weeks41 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

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  1. In T. Dietterich , D. Margineantu , F. Provost , and P. Turney , editors , Proceedings of the ICML'2000 Workshop on Cost-sensitive Learning. 2000 .]] In T. Dietterich, D. Margineantu, F. Provost, and P. Turney, editors, Proceedings of the ICML'2000 Workshop on Cost-sensitive Learning. 2000.]]
  2. In N. V. Chawla , N. Japkowicz , and A. Kotcz , editors , Proceedings of the ICML'2003 Workshop on Learning from Imbalanced Data Sets. 2003 .]] In N. V. Chawla, N. Japkowicz, and A. Kotcz, editors, Proceedings of the ICML'2003 Workshop on Learning from Imbalanced Data Sets. 2003.]]
  3. In C. Ferri , P. Flach , J. Orallo , and N. Lachice , editors , ECAI' 2004 First Workshop on ROC Analysis in AI. ECAI , 2004 .]] In C. Ferri, P. Flach, J. Orallo, and N. Lachice, editors, ECAI' 2004 First Workshop on ROC Analysis in AI. ECAI, 2004.]]
  4. 10.1145/1007730.1007735 · DOI · Paper page
  5. 10.1145/1007730.1007740 · DOI
  6. P. K. Chan and S. J. Stolfo . Toward scalable learning with non-uniform class and cost distributions: A case study in credit card fraud detection . In Proceedings of Knowledge Discovery and Data Mining , pages 164 -- 168 , 1998 .]] P. K. Chan and S. J. Stolfo. Toward scalable learning with non-uniform class and cost distributions: A case study in credit card fraud detection. In Proceedings of Knowledge Discovery and Data Mining, pages 164--168, 1998.]]
  7. N. V. Chawla . C4. 5 and imbalanced datasets: Investigating the effect of ampling method, probabilistic estimate, and decision tree structure . In Proceedings of the ICML'03 Workshipshop on Class Imbalances , 2003 .]] N. V. Chawla. C4.5 and imbalanced datasets: Investigating the effect of ampling method, probabilistic estimate, and decision tree structure. In Proceedings of the ICML'03 Workshipshop on Class Imbalances, 2003.]]
  8. 10.5555/1622407.1622416 · doi:10.5555/1622407.1622416
  9. 10.1007/978-3-540-39804-2_12 · DOI
  10. 10.1145/312129.312220 · DOI · Paper page
  11. 10.1145/347090.347126 · DOI
  12. C. Drummond and R. Holte . C4.5, class imbalance, and cost sensitivity: Why under-sampling beats over-sampling . In Proceedings of the ICML'03 Workshop on Learning from Imbalanced Data Sets , 2003 .]] C. Drummond and R. Holte. C4.5, class imbalance, and cost sensitivity: Why under-sampling beats over-sampling. In Proceedings of the ICML'03 Workshop on Learning from Imbalanced Data Sets, 2003.]]
  13. C. Elkan . The foundations of cost-sensitive learning . In Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence , pages 973 -- 978 , 2001 .]] C. Elkan. The foundations of cost-sensitive learning. In Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence, pages 973--978, 2001.]]
  14. W. Fan , S. Stolfo , J. Zhang , and P. Chan . Adacost: Misclassification cost-sensitive boosting . In Proceedings of Sixteenth International Conference on Machine Learning , pages 983 -- 990 , Slovenia , 1999 .]] W. Fan, S. Stolfo, J. Zhang, and P. Chan. Adacost: Misclassification cost-sensitive boosting. In Proceedings of Sixteenth International Conference on Machine Learning, pages 983--990, Slovenia, 1999.]]
  15. 10.5555/944919.944974 · doi:10.5555/944919.944974
  16. J. Furnkranz and P. Flach . An analysis of rule evaluation metrics . In Proceedings of the Twentieth International Conference on Machine Learning , pages 202 -- 209 , 2003 .]] J. Furnkranz and P. Flach. An analysis of rule evaluation metrics. In Proceedings of the Twentieth International Conference on Machine Learning, pages 202--209, 2003.]]
  17. 10.1145/1007730.1007736 · DOI · Paper page
  18. 10.5555/944919.944968 · doi:10.5555/944919.944968 · Paper page
  19. R. Hickey . Learning rare class footprints: the reflex algorithm . In Proceedings of the ICML'03 Workshop on Learning from Imbalanced Data Sets , 2003 .]] R. Hickey. Learning rare class footprints: the reflex algorithm. In Proceedings of the ICML'03 Workshop on Learning from Imbalanced Data Sets, 2003.]]
  20. R. Holte . Summary of the workshop. http://www.site./uottawa.ca/~nat/Workshop2003/workshop2003 .html, 2003 .]] R. Holte. Summary of the workshop. http://www.site./uottawa.ca/~nat/Workshop2003/workshop2003.html, 2003.]]
  21. 10.5555/647462.726288 · doi:10.5555/647462.726288
  22. 10.1023/A:1007660820062 · DOI
  23. N. Japkowics , Class imbalance : Are we focusing on the right issue? In Proceedings of the ICML'03 Workshop on Learning from Imbalanced Data Sets , 2003 .]] N. Japkowics, Class imbalance: Are we focusing on the right issue? In Proceedings of the ICML'03 Workshop on Learning from Imbalanced Data Sets, 2003.]]
  24. N. Japkowicz and R. Holte . Workshop report: Aaai-2000 workshop on learning from imbalanced data sets. AI Magazine, 22(1) , 2001 .]] N. Japkowicz and R. Holte. Workshop report: Aaai-2000 workshop on learning from imbalanced data sets. AI Magazine, 22(1), 2001.]]
  25. 10.1145/1007730.1007737 · DOI · Paper page
  26. 10.5555/645496.658037 · doi:10.5555/645496.658037
  27. P. Juszczak and R. P. W. Duin . Uncertainty sampling methods for one-class classifiers . In Proceedings of the ICML'03 Workshop on Learning from Imbalanced Data Sets , 2003 .]] P. Juszczak and R. P. W. Duin. Uncertainty sampling methods for one-class classifiers. In Proceedings of the ICML'03 Workshop on Learning from Imbalanced Data Sets, 2003.]]
  28. 10.1023/A:1012714523368 · DOI
  29. A. Kotcz , A. Chowdhury , and J. Alspector . Data duplication: An imbalance problem? In Proceedings of the ICML'2003 Workshop on Learning from Imbalanced Datasets , 2003 .]] A. Kotcz, A. Chowdhury, and J. Alspector. Data duplication: An imbalance problem? In Proceedings of the ICML'2003 Workshop on Learning from Imbalanced Datasets, 2003.]]
  30. M. Kubat and S. Matwin . Addressing the curse of imbalanced training sets: One sided selection . In Proceedings of the Fourteenth International Conference on Machine Learning , pages 179 -- 186 , Nashville, Tennesse , 1997 , Morgan Kaufmann.]] M. Kubat and S. Matwin. Addressing the curse of imbalanced training sets: One sided selection. In Proceedings of the Fourteenth International Conference on Machine Learning, pages 179--186, Nashville, Tennesse, 1997, Morgan Kaufmann.]]
  31. 10.5555/951949.952139 · doi:10.5555/951949.952139
  32. M. Maloof . Learning when data sets are imbalanced and when costs are unequal and unknown . In Proceedings of the ICML'03 Workshop on Learning from Imbalanced Data Sets , 2003 .]] M. Maloof. Learning when data sets are imbalanced and when costs are unequal and unknown. In Proceedings of the ICML'03 Workshop on Learning from Imbalanced Data Sets, 2003.]]
  33. L. M. Manevitz and M. Yousef . One-class SVMs for document classification . Journal of Machine Learning Research , 2 : 139 -- 154 , 2001 .]] L. M. Manevitz and M. Yousef. One-class SVMs for document classification. Journal of Machine Learning Research, 2:139--154, 2001.]]
  34. D. Mladenic and M. Grobelnik . Feature selection for unbalanced class distribution and naive bayes . In Proceedings of the 16th International Conference on Machine Learning , pages 258 -- 267 , 1999 .]] D. Mladenic and M. Grobelnik. Feature selection for unbalanced class distribution and naive bayes. In Proceedings of the 16th International Conference on Machine Learning, pages 258--267, 1999.]]
  35. R. Pearson , G. Goney , and J. Shwaber . Imbalanced clustering for microarray time-series . In Proceedings of the ICML'03 Workshop on Learning from Imbalanced Data Sets , 2003 .]] R. Pearson, G. Goney, and J. Shwaber. Imbalanced clustering for microarray time-series. In Proceedings of the ICML'03 Workshop on Learning from Imbalanced Data Sets, 2003.]]
  36. 10.1145/1007730.1007738 · DOI · Paper page
  37. J. Quinlan . C4.5 : Programs for Machine Learning . Morgan Kaufmann , 1993 .]] J. Quinlan. C4.5: Programs for Machine Learning. Morgan Kaufmann, 1993.]]
  38. 10.1016/j.jbi.2004.07.008 · DOI
  39. B. Raskutti and A. Kowalczyk . Extreme re-balancing for SVM's: a case study . In Proceedings of the ICML'03 Workshop on Learning from Imbalanced Data Sets , 2003 .]] B. Raskutti and A. Kowalczyk. Extreme re-balancing for SVM's: a case study. In Proceedings of the ICML'03 Workshop on Learning from Imbalanced Data Sets, 2003.]]
  40. 10.1145/1007730.1007739 · DOI · Paper page
  41. K. M. Ting . A comparative study of cost-sensitive boosting algorithms . In Proceedings of Seventeenth International Conference on Machine Learning , pages 983 -- 990 , Stanford, CA , 2000 .]] K. M. Ting. A comparative study of cost-sensitive boosting algorithms. In Proceedings of Seventeenth International Conference on Machine Learning, pages 983--990, Stanford, CA, 2000.]]
  42. 10.1109/TKDE.2002.1000348 · DOI · Paper page
  43. P. Turney . Types of cost in inductive concept learning . In Proceedings of the ICML'2000 Workshop on Cost-Sensitive Learning , pages 15 -- 21 , 2000 .]] P. Turney. Types of cost in inductive concept learning. In Proceedings of the ICML'2000 Workshop on Cost-Sensitive Learning, pages 15--21, 2000.]]
  44. S. Visa and A. Ralescu . Learning imbalanced and overlapping classes using fuzzy sets . In Proceedings of the ICML'03 Workshop on Learning from Imbalanced Data Sets , 2003 .]] S. Visa and A. Ralescu. Learning imbalanced and overlapping classes using fuzzy sets. In Proceedings of the ICML'03 Workshop on Learning from Imbalanced Data Sets, 2003.]]
  45. 10.5555/1622434.1622445 · doi:10.5555/1622434.1622445
  46. G. Wu and E. Y. Chang . Class-boundary alignment for imbalanced dataset learning . In Proceedings of the ICML'03 Workshop on Learning from Imbalanced Data Sets , 2003 .]] G. Wu and E. Y. Chang. Class-boundary alignment for imbalanced dataset learning. In Proceedings of the ICML'03 Workshop on Learning from Imbalanced Data Sets, 2003.]]
  47. 10.1145/502512.502540 · DOI · Paper page
  48. 10.5555/951949.952181 · doi:10.5555/951949.952181
  49. J. Zhang and I. Mani . knn approach to unbalanced data distributions: A case study involving information extraction . In Proceedings of the ICML'2003 Workshop on Learning from Imbalanced Datasets , 2003 .]] J. Zhang and I. Mani. knn approach to unbalanced data distributions: A case study involving information extraction. In Proceedings of the ICML'2003 Workshop on Learning from Imbalanced Datasets, 2003.]]
  50. Z. Zheng and R. Srihari . Optimally combining positive and negative features for text categorization . In Proceedings of the ICML'03 Workshop on Learning from Imbalanced Date Sets , 2003 .]] Z. Zheng and R. Srihari. Optimally combining positive and negative features for text categorization. In Proceedings of the ICML'03 Workshop on Learning from Imbalanced Date Sets, 2003.]]
  51. 10.1145/1007730.1007741 · DOI · Paper page

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Metadata

Authors
Chawla, N. V., Japkowicz, N., & Kotcz, A.
Venue
ACM SIGKDD Explorations Newsletter
Publisher
Association for Computing Machinery (ACM)
Published
2004-06
Volume
6
Issue
1
Pages
1-6
ISSN
  • 1931-0145 (ISSN-L)
  • 1931-0153
DOI
10.1145/1007730.1007733
OpenAlex
W2053724458
Cited by
2,063
References
7
Type
journal article
Language
en
Open access
closed
Licence
www.acm.org/publications/policies/copyright_policy#Background (vor)

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