Analysis and Visualization of Classifier Performance: Comparison under Imprecise Class and Cost Distributions

Proceedings - 3rd International Conference on Knowledge Discovery and Data Mining, KDD 1997

Volumn , Issue , 1997, Pages 43-48

  Provost, Foster ^a   Fawcett, Tom ^a  

^a VERIZON   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; COST BENEFIT ANALYSIS; DATA MINING; LEARNING SYSTEMS; SENSITIVITY ANALYSIS;

CLASS DISTRIBUTIONS; CLASSIFIER PERFORMANCE; COMPARISON OF CLASSIFIERS; COST DISTRIBUTION; DATA MINING PROBLEMS; DISTRIBUTION COSTS; INDUCTIVE LEARNING; MISCLASSIFICATION COSTS; PERFORMANCE COMPARISON; REAL-WORLD;

COMPUTATIONAL GEOMETRY;

EID: 85101511266     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (19)

1. Barber, C; Dobkin, D.; and Huhdanpaa, H. 1993. The quickhull algorithm for convex hull. Technical Report GCG53, University of Minnesota. Available from ftp://geom.umn.edu/pub/software/qhull.tar.Z.
2. Beck, J. R., and Schultz, E. K. 1986. The use of ROC curves in test performance evaluation. Arch Pathol Lab Med 110:13-20.
3. Bloedorn, E.; Mani, I.; and MacMillan, T. 1996. Machine learning of user profiles: Representational issues. In Proceedings of Thirteenth National Conference on Artificial Intelligence, 433-438. Menlo Park, CA: AAAI Press.
4. Breiman, L.; Friedman, J.; Olshen, R.; and Stone, C. 1984. Classification and regression trees. Belmont, CA: Wadsworth International Group.
5. Catlett, J. 1995. Tailoring rulesets to misclassificatioin costs. In Proceedings of the 1995 Conference on AI and Statistics, 88-94.
6. Clearwater, S., and Stern, E. 1991. A rule-learning program in high energy physics event classification. Comp Physics Comm 67:159-182.
7. Draper, B.; Brodley, C; and Utgoff, P. 1994. Goal-directed classification using linear machine decision trees. IEEE Transactions on Pattern Analysis and Machine Intelligence 16(9):888-893.
8. Duda, R. O., and Hart, P. E. 1973. Pattern Classification and Scene Analysis. New York: John Wiley.
9. Egan, J. P. 1975. Signal Detection Theory and ROC Analysis. Series in Cognitition and Perception. New York: Academic Press.
10. Ezawa, K.; Singh, M.; and Norton, S. 1996. Learning goal oriented bayesian networks for telecommunications risk management. In Proceedings of IMLC-96, 139-147. San Francisco, CA: Morgan Kaufmann.
11. Fawcett, T., and Provost, F. 1996. Combining data mining and machine learning for effective user profiling. In Proceedings of KDD-96, 8-13. Menlo Park, CA: AAAI Press.
12. Pazzani, M.; Merz, C; Murphy, P.; Ali, K.; Hume, T.; and Brunk, C. 1994. Reducing misclassification costs. In Proc. 11th International Conference on Machine Learning, 217-225. Morgan Kaufmann.
13. Provost, F., and Buchanan, B. 1992. Inductive policy. In Proceedings of AAAI-92, 255-261. Menlo Park, CA: AAAI Press.
14. Provost, F., and Buchanan, B. 1995. Inductive policy: The pragmatics of bias selection. Machine Learning 20:35-61.
15. Saitta, L.; Giordana, A.; and Neri, F. 1995. What is the "real-world"? In IMLC-95 Workshop on Applying Machine Learning in Practice (Tech Rept. AIC-95-023 Naval Research Lab), 34-40.
16. Swets, J. 1988. Measuring the accuracy of diagnostic systems. Science 240:1285-1293.
17. Turney, P. 1995. Cost-sensitive classification: Empirical evaluation of a hybrid genetic decision tree induction algorithm. JAIR 2:369-409.
18. Turney, P. 1996. Cost sensitive learning bibliography. http://ai.iit.nrc.ca/bibliographies/cost-sensitive.html.
19. Weinstein, M. C, and Fineberg, H. V. 1980. Clinical Decision Analysis. Philadelphi, PA: W. B. Saunders Company.