Explicitly representing expected cost: An alternative to ROC representation

Proceeding of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Volumn , Issue , 2000, Pages 198-207

  Drummond, Chris ^a   Holte, Robert C ^a  

^a UNIVERSITY OF OTTAWA   (Canada)

Author keywords

Cost Sensitive Learning; ROC Analysis

Indexed keywords

ALGORITHMS; COSTS; CURVE FITTING; DATA MINING;

COST SENSITIVE LEARNING;

CLASSIFICATION (OF INFORMATION);

EID: 0034592774     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (13)

1. C. L. Blake and C. J. Merz. UCI repository of machine learning databases, University of California, Irvine, CA www.ics.uci.edu/̃mlearn/MLRepository.html, 1998.
2. L. Breiman, J. H. Friedman, R. A. Olshen, and C. J. Stone. Classification and Regression Trees. Wadsworth, Belmont, CA, 1984.
3. P. Domingos. Metacost: A general method for making classifiers cost-sensitive. In Proceedings of the Fifth International Conference on Knowledge Discovery and Data Mining, pages 155-164, Menlo Park, CA, 1999. AAAI Press.
4. T. Fawcett and F. Provost. Combining data mining and machine learning for effective user profiling. In Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, pages 8-13, Menlo Park, CA, 1996. AAAI Press.
5. T. Fawcett and F. Provost. Adaptive fraud detection. Journal of Data Mining and Knowledge Discovery, 1:195-215, 1997.
6. M. Kubat, R. C. Holte, and S. Matwin. Machine learning for the detection of oil spills in satellite radar images. Machine Learning, 30:195-215, 1998.
7. L. B. Lusted. Introduction to Medical Decision Making. Charles C. Thomas, Springfiled, Illinois, 1968.
8. M. Pazzani, C. Merz, P. Murphy, K. Ali, T. Hume, and C. Brunk. Reducing misclassification costs. In Proceedings of the Eleventh International Conference on Machine Learning, pages 217-225, San Francisco, 1997. Morgan Kaufmann.
9. F. Provost and T. Fawcett. Analysis and visualization of classifier performance: Comparison under imprecise class and cost distributions. In Proceedings of the Third International Conference on Knowledge Discovery and Data Mining, pages 43-48, Menlo Park, CA, 1997. AAAI Press.
10. F. Provost and T. Fawcett. Robust classification systems for imprecise environments. In Proceedings of the Fifteenth National Conference on Artificial Intelligence, pages 706-713, Menlo Park, CA, 1998. AAAI Press.
11. F. Provost, T. Fawcett, and R. Kohavi. The case against accuracy estimation for comparing induction algorithms. In Proceedings of the Fifteenth International Conference on Machine Learning, pages 43-48, San Francisco, 1998. Morgan Kaufmann.
12. J. A. Swets. Measuring the accuracy of diagnostic systems. Science, 240:1285-1293, 1988.
13. J. A. Swets and R. M. Pickett. Evaluation of diagnostic systems : methods from signal detection theory. Academic Press, New York, 1982.