Adjusting the outputs of a classifier to new a Priori probabilities: A simple procedure

Neural Computation

Volumn 14, Issue 1, 2002, Pages 21-41

  Saerens, Marco ^a,b   Latinne, Patrice ^a   Decaestecker, Christine ^a  

^a UNIVERSITÉ LIBRE DE BRUXELLES   (Belgium)

^b Smals Belgium   (Belgium)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; CLASSIFICATION; METHODOLOGY; PROBABILITY; SYSTEM ANALYSIS;

CLASSIFICATION; PROBABILITY; PROBABILITY THEORY; SUPPORT, NON-U.S. GOV'T;

EID: 0036134369     PISSN: 08997667     EISSN: None     Source Type: Journal    
DOI: 10.1162/089976602753284446     Document Type: Article

References (22)

1. Blake, C., Keogh, E., & Merz, C. (1998). UCI repository of machine learning databases. Irvine, CA: University of California, Department of Information and Computer Science. Available online at: http://www.ics.uci.edu/ ∼mlearn/MLRepository.html.
2. Breiman, L. (1998). Arcing classifiers. Annals of Statistics, 26, 801-849.
3. Castelli, V., & Cover, T. (1995). On the exponential value of labelled samples. Pattern Recognition Letters, 16, 105-111.
4. Decaestecker, C., Lopes, M.-B., Gordower, L., Camby, I., Cras, P., Martin, J.-J., Kiss, R., VandenBerg, S., & Salmon, I. (1997). Quantitative chromatin pattern description in feulgen-stained nuclei as a diagnostic tool to characterise the oligodendroglial and astroglial components in mixed oligoatrocytomas. Journal of Neuropathology and Experimental Neurology, 56, 391-402.
5. Dempster, A., Laird, N., & Rubin, D. (1977). Maximum likelihood from incomplete data via the EM algorithm (with discussion). Journal of the Royal Statistical Society B, 39, 1-38.
6. Ghahramani, Z., & Jordan, M. (1994). Supervised learning from incomplete data via an EM algorithm. In J. D. Cowan, G. Tesauro, & J. Alspector (Eds.), Advances in neural information processing systems, 6 (pp. 120-127). San Mateo, CA: Morgan Kaufmann.
7. Hand, D. (1981). Discrimination and classification. New York: Wiley.
8. Kish, L., & Frankel, M. (1974). Inference from complex samples (with discussion). Journal of the Royal Statistical Society B, 61, 1-37.
9. Lawless, J., Kalbfleisch, J., & Wild, C. (1999). Semiparametric methods for response-selective and missing data problems in regression. Journal of the Royal Statistical Society B, 61, 413-438.
10. McLachlan, G. (1992). Discriminant analysis and statistical pattern recognition. New York: Wiley.
11. McLachlan, G., & Basford, K. (1988). Mixture models, inference and applications to clustering. New York: Marcel Dekker.
12. McLachlan, G., & Krishnan, T. (1997). The EM algorithm and extensions. New York: Wiley.
13. Melsa, J., & Cohn, D. (1978). Decision and estimation theory. New York: McGraw-Hill.
14. Miller, D., & Uyar, S. (1997). A mixture of experts classifier with learning based on both labeled and unlabeled data. In M. Mozer, M. Jordan, & T. Petsche (Eds.), Advances in neural information processing systems, 9 (pp. 571-578). Cambridge, MA: MIT Press.
15. Mood, A., Graybill, F., & Boes, D. (1974). Introduction to the theory of statistics (3rd ed.). New York: McGraw-Hill.
16. Nigam, K., McCallum, A., Thrun, S., & Mitchell, T. (2000). Text classification from labeled and unlabeled documents using EM. Machine Learning, 39, 103-134.
17. Papoulis, A. (1991). Probability, random variables, and stochastic processes (3rd ed.), New York: McGraw-Hill.
18. Richard, M., & Lippmann, R. (1991). Neural network classifiers estimate Bayesian a posteriori probabilities. Neural Computation, 2, 461-483.
19. Saerens, M. (2000). Building cost functions minimizing to some summary statistics. IEEE Transactions on Neural Networks, 11, 1263-1271.
20. Scott, A., & Wild, C. (1997). Fitting regression models to case-control data by maximum likelihood. Biometrika, 84, 57-71.
21. Shahshahani, B., & Landgrebe, D. (1994). The effect of unlabeled samples in reducing the small sample size problem and mitigating the Hugues phenomenon. IEEE Transactions on Geoscience and Remote Sensing, 32, 1087-1095.
22. Towell, G. (1996). Using unlabeled data for supervised learning. In D. Touretzky, M. Mozer, & M. Hasselmo (Eds.), Advances in neural information processing systems, 8 (pp. 647-653). Cambridge, MA: MIT Press.