Editorial
Tóm tắt
Từ khóa
Tài liệu tham khảo
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.]]
N. Abe. Invited talk: Sampling approaches to learning from imbalanced datasets: active learning cost sensitive learning and beyond. http://www.site.uottawa.ca/~nat/Workshop2003/ICML03Workshop_Abe.ppt 2003.]] N. Abe. Invited talk: Sampling approaches to learning from imbalanced datasets: active learning cost sensitive learning and beyond. http://www.site.uottawa.ca/~nat/Workshop2003/ICML03Workshop_Abe.ppt 2003.]]
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.]]
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.]]
C. Elkan. Invited talk: The real challenges in data mining: A contrarian view. http://www.site.uottawa.ca/~nat/Workshop2003/realchallenges2.ppt. 2003.]] C. Elkan. Invited talk: The real challenges in data mining: A contrarian view. http://www.site.uottawa.ca/~nat/Workshop2003/realchallenges2.ppt. 2003.]]
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.]]
T. Fawcett ROC graphs: Notes and practical considerations for researchers. http://www.hpl.hp.com/personal/Tom_Fawcett/papers/index.html 2003.]] T. Fawcett ROC graphs: Notes and practical considerations for researchers. http://www.hpl.hp.com/personal/Tom_Fawcett/papers/index.html 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 .]] 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.]]
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.]]
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.]]
N. Japkowics and S. Stephen . The class imbalance problem: A systematic study . Intelligent Data Analysis , 6 ( 5 ): 203 -- 231 , 2002 .]] N. Japkowics and S. Stephen. The class imbalance problem: A systematic study. Intelligent Data Analysis, 6(5):203--231, 2002.]]
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.]]
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.]]
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.]]
F. Provost. Invited talk: Choosing a marginal class distribution for classifier induction. http://www.site.uottawa.ca/~nat/Workshop2003/provost.html 2003.]] F. Provost. Invited talk: Choosing a marginal class distribution for classifier induction. http://www.site.uottawa.ca/~nat/Workshop2003/provost.html 2003.]]
J. Quinlan . C4.5 : Programs for Machine Learning . Morgan Kaufmann , 1993 .]] J. Quinlan. C4.5: Programs for Machine Learning. Morgan Kaufmann, 1993.]]
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.]]
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.]]
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.]]
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.]]
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.]]