Explicitly representing expected cost

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Explicitly representing expected cost: an alternative to ROC representation

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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: 198 - 207

Year of Publication: 2000

ISBN:1-58113-233-6

Authors

Chris Drummond	School of Information Technology and Engineering, University of Ottawa, Ottawa, Ontario, Canada, K1N 6N5
Robert C. Holte	School of Information Technology and Engineering, University of Ottawa, Ottawa, Ontario, Canada, K1N 6N5

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): 7, Downloads (12 Months): 62, Citation Count: 19

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references cited by index terms collaborative colleagues

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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.

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	13	J. A. Swets and R. M. Pickett. Evaluation of diagnostic systems : methods from signal detection theory. Academic Press, New York, 1982.

CITED BY 19

	Tom Fawcett, "In vivo" spam filtering: a challenge problem for KDD, ACM SIGKDD Explorations Newsletter, v.5 n.2, December 2003
	Zhi-Hua Zhou , Xu-Ying Liu, Training Cost-Sensitive Neural Networks with Methods Addressing the Class Imbalance Problem, IEEE Transactions on Knowledge and Data Engineering, v.18 n.1, p.63-77, January 2006
	Nitesh V. Chawla , Nathalie Japkowicz , Aleksander Kotcz, Editorial: special issue on learning from imbalanced data sets, ACM SIGKDD Explorations Newsletter, v.6 n.1, June 2004
	Jeremy Z. Kolter , Marcus A. Maloof, Learning to detect malicious executables in the wild, Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, August 22-25, 2004, Seattle, WA, USA
	Nitesh V. Chawla , Lawrence O. Hall , Ajay Joshi, Wrapper-based computation and evaluation of sampling methods for imbalanced datasets, Proceedings of the 1st international workshop on Utility-based data mining, p.24-33, August 21-21, 2005, Chicago, Illinois
	Chunsheng Yang , Sylvain Létourneau, Learning to predict train wheel failures, Proceeding of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining, August 21-24, 2005, Chicago, Illinois, USA
	Robert C. Holte , Chris Drummond, Cost-sensitive classifier evaluation, Proceedings of the 1st international workshop on Utility-based data mining, August 21-21, 2005, Chicago, Illinois
	Foster Provost , Tom Fawcett, Robust Classification for Imprecise Environments, Machine Learning, v.42 n.3, p.203-231, March 2001
	Chris Drummond , Robert C. Holte, Cost curves: An improved method for visualizing classifier performance, Machine Learning, v.65 n.1, p.95-130, October 2006
	Jesse Davis , Mark Goadrich, The relationship between Precision-Recall and ROC curves, Proceedings of the 23rd international conference on Machine learning, p.233-240, June 25-29, 2006, Pittsburgh, Pennsylvania
	Tom Fawcett, ROC graphs with instance-varying costs, Pattern Recognition Letters, v.27 n.8, p.882-891, June 2006
	Gary M. Weiss , Ye Tian, Maximizing classifier utility when training data is costly, ACM SIGKDD Explorations Newsletter, v.8 n.2, p.31-38, December 2006
	Rocío Alaiz-Rodríguez , Alicia Guerrero-Curieses , Jesús Cid-Sueiro, Minimax Regret Classifier for Imprecise Class Distributions, The Journal of Machine Learning Research, 8, p.103-130, 5/1/2007
	J. Zico Kolter , Marcus A. Maloof, Learning to Detect and Classify Malicious Executables in the Wild, The Journal of Machine Learning Research, 7, p.2721-2744, 12/1/2006
	Tadeusz Pietraszek, Classification of intrusion detection alerts using abstaining classifiers, Intelligent Data Analysis, v.11 n.3, p.293-316, August 2007
	Charles Dugas , David Gadoury, Pointwise exact bootstrap distributions of cost curves, Proceedings of the 25th international conference on Machine learning, p.280-287, July 05-09, 2008, Helsinki, Finland
	Zhi-Hua Zhou , Xu-Ying Liu, On multi-class cost-sensitive learning, Proceedings of the 21st national conference on Artificial intelligence, p.567-572, July 16-20, 2006, Boston, Massachusetts
	Alvaro A. Cárdenas , John S. Baras, B-ROC curves for the assessment of classifiers over imbalanced data sets, proceedings of the 21st national conference on Artificial intelligence, p.1581-1584, July 16-20, 2006, Boston, Massachusetts
	Nitesh V. Chawla , Kevin W. Bowyer , Lawrence O. Hall , W. Philip Kegelmeyer, SMOTE: synthetic minority over-sampling technique, Journal of Artificial Intelligence Research, v.16 n.1, p.321-357, January 2002