Multiple reject thresholds for improving classification reliability

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 1876 LNCS, Issue , 2000, Pages 863-871

  Fumera, Giorgio ^a   Roli, Fabio ^a   Giacinto, Giorgio ^a  

^a UNIVERSITY OF CAGLIARI   (Italy)

Author keywords

[No Author keywords available]

Indexed keywords

ECONOMIC AND SOCIAL EFFECTS; ERRORS; PROBABILITY; REMOTE SENSING; IMAGE RECONSTRUCTION; PATTERN RECOGNITION SYSTEMS;

A-POSTERIORI PROBABILITIES; DATA CLASS; MULTISENSOR REMOTE SENSING; OPTIMAL ERROR; OPTIMALITY; RULE BASED; TRADE OFF;

PATTERN RECOGNITION SYSTEMS; PATTERN RECOGNITION;

EID: 84877747933     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-44522-6_89     Document Type: Conference Paper

References (8)

1. Chow, C.K.: On Optimum Error and Reject Trade-off. IEEE Transactions on Information Theory, Vol. It-16, 1 (1970) 41-46
2. Cordella, L.P., De Stefano, C., Tortorella, F., Vento, M.: A Method for Improving Classification Reliability of Multilayer Perceptrons. IEEE Transactions on Neural Networks, Vol. 6, 5 (1995) 1140-1147
3. Yau, H.C., Manry, M.T.: Automatic Determination of Reject Thresholds in Classifiers Employing Discriminant Functions. IEEE Transactions on Signal Processing, Vol. 40, 3 (1992) 711-713
4. Battiti R. and Colla A.M. Democracy in neural Nets: Voting Schemes for Classification. Neural Networks, 7, (1994) 691-707
5. Tumer, K. and J. Ghosh. Error correlation and error reduction in ensemble classifiers, Connection Science, 8, (1996) 385-404.
6. G. Fumera and F. Roli: Multiple Reject Thresholds for Improving Classification Reliability, Internal Report, Univ. of Cagliari, 1999
7. Roli F.: Multisensor image recognition by neural networks with understandable behaviour. International Journal of Pattern Recognition and Artificial Intelligence 10 (1996) 887-917.
8. Serpico, S.B., and Roli F.: Classification of multi-sensor remote-sensing images by structured neural networks, IEEE Trans. On Geoscience and Remote Sensing 33 (1995) 562-578.