Prodding the ROC curve: Constrained optimization of classifier performance

Advances in Neural Information Processing Systems

Volumn , Issue , 2002, Pages

  Mozer, Michael C ^a,b   Dodier, Robert ^a   Colagrosso, Michael D ^a,b   Guerra Salcedo, César ^a   Wolniewicz, Richard ^a  

^a Athene Software Inc   (United States)

^b UCB 450   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CONSTRAINED OPTIMIZATION; TELECOMMUNICATION INDUSTRY;

BUSINESS APPLICATIONS; CLASSIFIER PERFORMANCE; DOMAIN CONSTRAINT; FINANCIAL RETURNS; MACHINE-LEARNING; REJECTION RATES; SERVICE PROVIDER; TELECOMMUNICATIONS INDUSTRY;

ALGORITHMS;

EID: 33746071820     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (6)

1. Bertsekas, D. P. (1982). Constrained optimization and Lagrange multiplier methods. NY: Academic.
2. Chang, E. I., & Lippmann, R. P. (1994). Figure of merit training for detection and spotting. In J. D. Cowan, G. Tesauro, & J. Alspector (Eds.), Advances in Neural Information Processing Systems 6 (1019-1026). San Mateo, CA: Morgan Kaufmann.
3. Frederick, E. D., & Floyd, C. E. (1998). Analysis of mammographic findings and patient history data with genetic algorithms for the prediction of breast cancer biopsy outcome. Proceedings of the SPIE, 3338, 241-245.
4. Green, D. M., & Swets, J. A. (1966). Signal detection theory and psychophysics. New York: Wiley.
5. Mozer, M. C., Wolniewicz, R., Grimes, D., Johnson, E., & Kaushansky, H. (2000). Maximizing revenue by predicting and addressing customer dissatisfaction. IEEE Transactions on Neural Networks, 11, 690-696.
6. Whitley, D. (1989). The GENITOR algorithm and selective pressure: Why rank-based allocation of reproductive trials is best. In D. Schaffer (Ed.), Proceedings of the Third International Conference on Genetic Algorithms (pp. 116-121). San Mateo, CA: Morgan Kaufmann.