Reliability estimation of a statistical classifier

Pattern Recognition Letters

Volumn 29, Issue 3, 2008, Pages 243-253

  Devarakota, Pandu Ranga Rao ^a,b   Mirbach, Bruno ^a   Ottersten, Björn ^b  

^a IEE S A   (Luxembourg)

^b ROYAL INSTITUTE OF TECHNOLOGY   (Sweden)

Author keywords

Binomial distribution; Confidence intervals; GMMs; Local density estimation; Pattern classification; Pattern rejection

Indexed keywords

DATA ACQUISITION; LOCAL DENSITY APPROXIMATION; PATTERN RECOGNITION; PROBLEM SOLVING; RELIABILITY THEORY;

BINOMIAL DISTRIBUTION; CONFIDENCE INTERVALS; PATTERN REJECTION;

STATISTICAL METHODS;

EID: 37049017079     PISSN: 01678655     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patrec.2007.09.019     Document Type: Article

References (31)

1. Bilmes, J., 1997. A gentle tutorial on the EM algorithm and its application to parameter estimation for Gaussian mixture and hidden Markov models. .
2. Blake, C., Keogh, E., Merz, C., 1998. UCI Repository of Machine Learning Databases. Department of Information and Computer Sciences, University of California, Irvine. .
3. Brown L.D., Tony Cai T., and DasGupta A. Interval estimation for a binomial proportion. Statist. Sci. 16 (2001) 101-117
4. Chow C.K. On optimum error and reject trade-off. IEEE Trans. Inf. Theory 16 (1970) 41-46
5. Devarakota P.R.R., Mirbach B., Castillo-Franco M., and Ottersten B. Occupant classification using range images. IEEE Trans. Veh. Technol. 56 4 (2007) 1983-1993
6. Dubuisson B., and Masson M. A statistical decision rule with incomplete knowledge about classes. Pattern Recognit. 26 (1993) 155-165
7. Duda R.O., and Hart P.E. Pattern Classification and Scene Analysis (1991), Wiley Publication
8. Duin R.P.W. On the choice of smoothing parameters for parzen estimators of probability density functions. IEEE Trans. Comput. 25 (1970) 1175-1179
9. Duin R.P.W., and Tax D.M.J. Classifier conditional posterior probabilities. In: Amin A., Dori D., Pudil P., and Freeman H. (Eds). Advances in Pattern Recognition Proc. Joint IAPR Internat. Workshops SSPR'98 and SPR'98 Sydney, Australia, August 1998. Lecture Notes in Computer Science vol. 1451 (1998), Springer, Berlin 611-619
10. Figueiredo M.A.T., and Jain A.K. Unsupervised learning of finite mixture models. IEEE Trans. Pattern Anal. Machine Intell. 24 (2002) 1-16
11. Fumera G., Roli F., and Giacinto G. Reject option with multiple thresholds. Pattern Recognit. 33 (2000) 2099-2111
12. Ha T.M. The optimum class-selective rejection rule. IEEE Trans. Pattern Anal. Machine Intell. 19 (1997) 608-615
13. Jain A.K., Duin R.P.W., and Mao J. Statistical pattern recognition: A review. IEEE Trans. Pattern Anal. Machine Intell. 22 (2000) 4-37
14. Kuncheva, L.I., 2000. Clustering-and-selection model for classifier combination. In: Proc. Knowledge-based Intelligent Engineering Systems and Allied Technologies. Bringhton, UK, 2000, pp. 185-188.
15. Lanckriet G.R.G., Ghaoui L.E., and Jordan M.I. Robust novelty detection with single-class mpm. In: Becker S., Thrun S., and Obermayer K. (Eds). Advances in Neural Information Processing Systems vol. 15 (2003), MIT Press, Cambridge, MA
16. Leonard J.A., Kramer M.A., and Ungar L.H. A neural network architecture that computes its own reliability. Comput. Chem. Eng. 16 (1992) 819-835
17. Li M., and Sethi I. Confidence-based active learning. IEEE Trans. Pattern Anal. Machine Intell. 28 (2006) 1251-1261
18. α distance between posterior density function. IEEE Trans. Inf. Theory 22 (1976) 34-45
19. Liu R., and Yuan B. Multiple classifiers combination by clustering and selection. Inf. Fusion 2 (2001) 163-168
20. Paalanen P., Kamarinen J.K., Ilonen J., and Kälviäinen H. Feature representation and discrimination based on Gaussian mixture model probability densities - Practices and algorithms. Pattern Recognit. 39 (2006) 1346-1358
21. Platt, J., 2000. Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. In: Smola, A., Bartlett, P., Schölkopf, B., Schuurmans, D. (Eds.), Advances in Large Margin Classifiers.
22. Redner R.A., and Walker H.F. Mixture densities, maximum likelihood and the EM algorithm. SIAM Rev. 26 2 (1984) 195-239
23. Schürmann J. Pattern Classification: Statistical and Neural Network based Approach (1990), John Wiley and Sons Inc., New York
24. Tarassenko, L., Nairac, A., Townsend, N., Cowley, P., 1999. Novelty detection in jet engines. In: IEE Colloquium on Condition Monitoring, Imagery, External Structures and Health, pp. 41-45.
25. Tax, D.M.J., 2007. DDtools, the Data Description Toolbox for Matlab, version 1.5.7, April 2007.
26. Tax D., and Duin R. Outlier detection using classifier instability. In: Amin A., Dori D., and Freeman H. (Eds). Advances in Pattern Recognition Proc. Joint IAPR Internat. Workshops SSPR'98 and SPR'98 Sydney, Australia. Lecture Notes in Computer Science vol. 1451 (1998), Springer, Berlin 593-601
27. Tax D., and Duin R. Support vector domain description. Pattern Recognition Lett. 20 (1999) 1191-1199
28. Tax D., and Duin R. Support vector domain description. Mach. Learn. 54 1 (2004) 45-66
29. Vapnik V.N. Statistical Learning Theory (1998), John Wiley and Sons, New York
30. Woods K., Philip Kegelmeyer Jr. W., and Bowyer K. Combination of multiple classifiers using local accuracy estimates. IEEE Trans. PAMI 19 4 (1997) 405-410
31. Zadrozny, B., Elkan, C., 2002. Transforming classifier scores into accurate multiclass probability estimates. In: Proc. 8th ACM SIGKDD Internat. Conf. on Knowledge Discovery and Data Mining.