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MetaCost: a general method for making classifiers cost-sensitive
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Source International Conference on Knowledge Discovery and Data Mining archive
Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining table of contents
San Diego, California, United States
Pages: 155 - 164  
Year of Publication: 1999
ISBN:1-58113-143-7
Author
Pedro Domingos  Artificial Intelligence Group, Instituto Superior Ténico, Lisbon 1049-001, Portugal
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
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Downloads (6 Weeks): 20,   Downloads (12 Months): 150,   Citation Count: 92
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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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C. Blake, E. Keogh, and C. J. Merz. UCI repository of machine learning databases. Dept. of Information and Computer Science, University of California at Irvine, CA, 1999. http://www.ics.uci.edu/- -~mlearn/MLP~epository. html.
 
5
 
6
L. Breiman. Pasting bites together for prediction in large data sets and on-line. Technical report, Statistics Dept., University of California at Berkeley, CA, 1996.
 
7
L. Breiman. Out-of-bag estimation. Technical report, Statistics Dept., University of California at Berkeley, CA, 1998.
 
8
L. Breiman, J. H. Friedman, R. A. Olshen, and C. J. Stone. Classification and Regression Trees. Wadsworth, Belmont, CA, 1984.
 
9
P. Chan and S. Stolfo. Toward scalable learning with non-uniform class and cost distributions. Proc. J th Intl. Conf. on Knowledge Discovery and Data Mining, pp. 164-168, New York, NY, 1998.
 
10
P. Chan, S. Stolfo, and D. Wolpert, editors. Proc. AAAI-96 Wkshp. on Integrating Multiple Learned Models for Improving and Scaling Machine Learning Algorithms. AAAI Press, Portland, OR, 1996.
 
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B. W. Dasarathy, editor. Nearest Neighbor (NN) Norms: NN Pattern Classification Techniques. IEEE Computer Society Press, Los Alamitos, CA, 1991.
 
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P. Domingos. Linear-time rule induction. Proc. ~nd Intl. Conf. on Knowledge Discovery and Data Mining, pp. 96-101, Portland, OR, 1996.
 
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P. Domingos. Why does bagging work? A Bayesian account and its implications. Proc. 3rd Intl. Conf. on Knowledge Discovery and Data Mining, pp. 155-158, Newport Beach, CA, 1997.
 
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P. Domingos. How to get a free lunch: A simple cost model for machine learning applications. Proc. AAAi-98/ICML-98 Wkshp. on the Methodology of Applying Machine Learning, pp. 1-7, Madison, WI, 1998.
 
16
 
17
R. O. Duda and P. E. Hart. Pattern Classification and Scene Analysis. Wiley, New York, NY, 1973.
 
18
 
19
Y. Freund and R. E. Schapire. Experiments with a new boosting algorithm. Proc. 13th Intl. Conf. on Machine Learning, pp. 148-156, Bari, Italy, 1996.
 
20
R. S. Michalski. A theory and methodology of inductive learning. Artificial Intelligence, 20:111- 161, 1983.
 
21
F. Provost and T. Fawcett. Analysis and visualization of classifier performance. Proc. 3rd Intl. Conf. on Knowledge Discovery and Data Mining, pp. 43-48, Newport Beach, CA, 1997.
 
22
 
23
 
24
 
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P. Turney. Cost-sensitive learning bibliography. Online bibliography, Institute for Information Technology of the National Research Council of Canada, Ottawa, Canada, 1997. http://- ai.iit.nrc, ca/bibliographies/cost-sensitive.html.
 
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CITED BY  92