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Editorial: special issue on learning from imbalanced data sets
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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SMOTE synthetic minority over-sampling technique
Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002) · Journal of Artificial Intelligence Research · doi:10.1613/jair.953 · Cited by 32,865
Self-citationEstimating the Support of a High-Dimensional Distribution
Schölkopf, B., Platt, J. C., Shawe-Taylor, J., Smola, A. J., & Williamson, R. C. (2001) · Neural Computation · doi:10.1162/089976601750264965 · Cited by 6,168
The class imbalance problem: A systematic study1
Japkowicz, N., & Stephen, S. (2002) · Intelligent Data Analysis · doi:10.3233/ida-2002-6504 · Cited by 3,295
Self-citationText classification from labeled and unlabeled documents using EM
Nigam, K., Mccallum, A. K., Thrun, S., & Mitchell, T. (2000) · Machine Learning · doi:10.1023/a:1007692713085 · Cited by 2,736
Mining with rarity: a unifying framework
Weiss, G. M. (2004) · ACM SIGKDD Explorations Newsletter · doi:10.1145/1007730.1007734 · Cited by 1,393
Robust classification for imprecise environments
Provost, F., & Fawcett, T. (2001) · Machine Learning · doi:10.1023/a:1007601015854 · Cited by 1,250
Cost-sensitive learning by cost-proportionate example weighting
Zadrozny, B., Langford, J., & Abe, N. (2004) · Third IEEE International Conference on Data Mining · doi:10.1109/icdm.2003.1250950 · Cited by 680
Research works printed but not linked: 51
- 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.]]
- 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.]]
- 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.]]
- 10.1145/1007730.1007735 · DOI · Paper page
- 10.1145/1007730.1007740 · DOI
- 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.]]
- 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.]]
- 10.5555/1622407.1622416 · doi:10.5555/1622407.1622416
- 10.1007/978-3-540-39804-2_12 · DOI
- 10.1145/312129.312220 · DOI · Paper page
- 10.1145/347090.347126 · DOI
- 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.]]
- 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.]]
- 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.]]
- 10.5555/944919.944974 · doi:10.5555/944919.944974
- 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.]]
- 10.1145/1007730.1007736 · DOI · Paper page
- 10.5555/944919.944968 · doi:10.5555/944919.944968 · Paper page
- 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.]]
- 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.]]
- 10.5555/647462.726288 · doi:10.5555/647462.726288
- 10.1023/A:1007660820062 · DOI
- 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.]]
- 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.]]
- 10.1145/1007730.1007737 · DOI · Paper page
- 10.5555/645496.658037 · doi:10.5555/645496.658037
- 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.]]
- 10.1023/A:1012714523368 · DOI
- 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.]]
- 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.]]
- 10.5555/951949.952139 · doi:10.5555/951949.952139
- 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.]]
- 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.]]
- 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.]]
- 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.]]
- 10.1145/1007730.1007738 · DOI · Paper page
- J. Quinlan . C4.5 : Programs for Machine Learning . Morgan Kaufmann , 1993 .]] J. Quinlan. C4.5: Programs for Machine Learning. Morgan Kaufmann, 1993.]]
- 10.1016/j.jbi.2004.07.008 · DOI
- 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.]]
- 10.1145/1007730.1007739 · DOI · Paper page
- 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.]]
- 10.1109/TKDE.2002.1000348 · DOI · Paper page
- 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.]]
- 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.]]
- 10.5555/1622434.1622445 · doi:10.5555/1622434.1622445
- 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.]]
- 10.1145/502512.502540 · DOI · Paper page
- 10.5555/951949.952181 · doi:10.5555/951949.952181
- 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.]]
- 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.]]
- 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
- Published
- 2004-06
- Volume
- 6
- Issue
- 1
- Pages
- 1-6
- ISSN
- 1931-0145 (ISSN-L)
- 1931-0153
- OpenAlex
- W2053724458
- Cited by
- 2,063
- References
- 7
- Type
- journal article
- Language
- en
- Open access
- closed
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