Classifier fusion using triangular norms

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

Volumn 3077, Issue , 2004, Pages 154-163

  Bonissone, Piero ^a   Goebel, Kai ^a   Yan, Weizhong ^a  

^a GE GLOBAL RESEARCH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; COMPUTER SCIENCE; COMPUTERS;

CLASSIFIER FUSION; CONFIDENCE MEASURE; DEMPSTER-SHAFER; FUSION RULE; GENERALIZED INTERSECTION; POSITIVE CORRELATIONS; T - NORM; TRIANGULAR NORMS;

MATHEMATICAL OPERATORS;

EID: 35048828303     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-25966-4_15     Document Type: Article

References (18)

1. Al-Ani, A. and Deriche, M. A new technique for combining multiple classifiers using the Dempster-Shafer theory of evidence, J. Artificial Intelligence Research, 17:333-361, 2002.
2. Bonissone, P.P. and Decker, K.S. Selecting Uncertainty Calculi and Granularity: An Experiment in Trading-off Precision and Complexity, in Kanal and Lemmer (editors) Uncertainty in Artificial Intelligence, pages 217-247, North-Holland, 1986.
3. Bonissone, P.P. Summarizing and Propagating Uncertain Information with Triangular Norms, International Journal of Approximate Reasoning, 1(1):71-101, January 1987.
4. Bonissone, P.P. Automating the Quality Assurance of an On-line Knowledge-Based Classifier By Fusing Multiple Off-line Classifiers, Proc. IPMU 2004, Perugia (Italy), July 2004.
5. Cho, S. and Kim, J., Combining multiple neural networks by fuzzy integral for robust classification, IEEE Trans. on Systems, Man, and Cybernetics, 25(2):380-384, 1995
6. Dempster, A.P. Upper and lower probabilities induced by a multivalued mapping. Annals of Mathematical Statistics, 38:325-339, 1967.
7. Ho, T.K., Hull, J.J., and Srihari, S.N. Decision Combination in Multiple Classifier Systems, IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 16, No.1, pp.66-75, 1994.
8. th International Conference on Document Analysis and Recognition, 2:459-462, 1997.
9. Kuncheva L.I. Switching between selection and fusion in combining classifiers: An experiment, IEEE Transactions on SMC, Part B, 32 (2), 2002, 146-156.
10. Lam L. and Suen, C. Y. Increasing experts for majority vote in OCR: Theoretical considerations and strategies. Proc. 4th Int. Workshop on Frontiers in Handwriting Recognition, Taipei, Taiwan, 245-254, 1994.
11. Niyogi, P Pierrot, J-B, and Siohan.O. On decorrelating classifiers and combining them, MIT AI Lab., September 2001.
12. Partridge, D., Yates, W.B. Engineering multiversion neural-net systems. Neural Computation, 8:869-893, 1996.
13. Petrakos, M., Kannelopoulos, I., Benediktsson, J., and Pesaresi, M. The Effect of Correlation on the Accuracy of the Combined Classifier in Decision Level Fusion, Proceedings of IEEE 2000 International Geo-science and Remote Sensing Symposium, Vol. 6, 2000.
14. Roli, F., Giacinto, G., and Vernazza, G. Methods for Designing Multiple Classifier Systems, MCS 200, LNCS 2096, 78-87, 2001.
15. Schweizer, B., and Sklar, A. Associative Functions and Abstract Semi-Groups,. Publicationes Mathematicae Debrecen, 10:69-81, 1963.
16. Shafer, G. A Mathematical Theory of Evidence, Princeton University Press, NJ, 1976.
17. Turner, K.& Ghosh, J. Error correlation and error reduction in ensemble classifiers, Connection Science, 8:385-404, 1996.
18. Xu, L., Krzyzak, A., & Suen, C.Y. Methods of Combining Multiple Classifiers and Their Applications to Handwriting Recognition, IEEE Syst. Man and Cybernetics, 22(3), 1992.