Decision templates for multiple classifier fusion: an experimental comparison

Pattern Recognition

Volumn 34, Issue 2, 2001, Pages 299-314

  Kuncheva, Ludmila I ^a,d   Bezdek, James C ^b,e,f   Duin, Robert P W ^c  

^a BANGOR UNIVERSITY   (United Kingdom)

^b UNIVERSITY OF WEST FLORIDA   (United States)

^c DELFT UNIVERSITY OF TECHNOLOGY   (Netherlands)

^d TECHNICAL UNIVERSITY OF SOFIA   (Bulgaria)

^e UNIVERSITY OF NEVADA   (United States)

^f Department of Obstetrics Gynecology   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMBINATORIAL MATHEMATICS; DECISION TABLES; FUZZY SETS; KNOWLEDGE BASED SYSTEMS; MATHEMATICAL MODELS; PATTERN MATCHING; PROBABILITY;

CLASS CONSCIOUS FUSION; CLASS INDIFFERENT FUSION; DECISION TEMPLATES; FUZZY INTEGRAL; FUZZY SIMILARITY; MULTIPLE CLASSIFIER FUSION;

PATTERN RECOGNITION SYSTEMS;

EID: 0034830461     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0031-3203(99)00223-X     Document Type: Article

References (50)

1. L. Lam, C.Y. Suen, Optimal combination of pattern classifiers, Pattern Recognition Lett. 16 (1995) 945-954.
2. G. Rogova, Combining the results of several neural network classifiers, Neural Networks 7 (1994) 777-781.
3. K. Woods, W.P. Kegelmeyer, K. Bowyer, Combination of multiple classifiers using local accuracy estimates, IEEE Trans. Pattern Anal. Mach. Intell. 19 (1997) 405-410.
4. L. Xu, A. Krzyzak, C.Y. Suen, Methods of combining multiple classifiers and their application to handwriting recognition, IEEE Trans. Systems Man Cybernet. 22 (1992) 418-435.
5. J. Kittler, M. Hatef, R.P.W. Duin, J. Matas, On combining classifiers, IEEE Trans. Pattern Anal. Mach. Intell. 20 (3) (1998) 226-239.
6. S.-B. Cho, J.H. Kim, Combining multiple neural networks by fuzzy integral and robust classification, IEEE Trans. Systems Man Cybernet. 25 (1995) 380-384.
7. P.D. Gader, M.A. Mohamed, J.M. Keller, Fusion of handwritten word classifiers, Pattern Recognition Lett. 17 (1996) 577-584.
8. M. Grabisch, F. Dispot, A comparison of some for fuzzy classification on real data, Proceedings of the Second International Conference on Fuzzy Logic and Neural Networks, lizuka, Japan, 1992, pp. 659-662.
9. J.M. Keller, P. Gader, H. Tahani, J.-H. Chiang, M. Mohamed, Advances in fuzzy integration for pattern recognition, Fuzzy Sets and Systems 65 (1994) 273-283.
10. I. Bloch, Information combination operators for data fusion: a comparative review with classification, IEEE Trans. Systems Man Cybernet. - Part A: Systems Humans 26 (1996) 52-67.
11. R.A. Jacobs, M.I. Jordan, S.J. Nowlan, G.E. Hinton, Adaptive mixtures of local experts, Neural Comput. 3 (1991) 79-87.
12. R.A. Jacobs, Methods for combining experts' probability assessments, Neural Comput. 7 (1995) 867-888.
13. M.I. Jordan, L. Xu, Convergence results for the EM approach to mixtures of experts architectures, Neural Networks 8 (1995) 1409-1431.
14. S.J. Nowlan, G.E. Hinton, Evaluation of adaptive mixtures of competing experts, in: R.P. Lippmann, J.E. Moody, D.S. Touretzky (Eds.), Advances in Neural Information Processing Systems, Vol. 3, 1991, pp. 774-780.
15. C.M. Bishop, Neural Networks for Pattern Recognition, Clarendon Press, Oxford, 1995.
16. H. Drucker, C. Cortes, L.D. Jackel, Y. LeCun, V. Vapnik, Boosting and other ensemble methods, Neural Comput. 6 (1994) 1289-1301.
17. J.A. Benediktsson, P.H. Swain, Consenus theoretic classification methods, IEEE Trans. Systems Man Cybernet. 22 (1992) 688-704.
18. K.-C. Ng, B. Abramson, Consensus diagnosis: a simulation study, IEEE Trans. Systems Man Cybernet. 22 (1992) 916-928.
19. J.A. Benediktsson, J.R. Sveinsson, J.I. Ingimundarson, H. Sigurdsson, O.K. Ersoy, Multistage classifiers optimized by neural networks and genetic algorithms, Non-linear Anal. Theory Methods Appl. 30 (3) (1997) 1323-1334.
20. R. Battiti, A.M. Colla, Democracy in neural nets: voting schemes for classification, Neural Networks 7 (1994) 691-707.
21. B.V. Dasarathy, B.V. Sheela, A composite classifier system design: concepts and methodology, Proc. IEEE 67 (1978) 708-713.
22. E. Filippi, M. Costa, E. Pasero, Multi-layer perceptron ensembles for increased performance and fault-tolerance in pattern recognition tasks, IEEE International Conference on Neural Networks, Orlando, FL, 1994, pp. 2901-2906.
23. C.-C. Chiang, H.-C. Fu, A divide-and-conquer methodology for modular supervised neural network design, IEEE International Conference on Neural Networks, Orlando, FL, 1994, pp. 119-124.
24. F. Smieja, The pandemonium system of reflective agents, IEEE Trans. Neural Networks 7 (1996) 97-106.
25. L.I. Kuncheva, Change-glasses approach in pattern recognition, Pattern Recognition Lett. 14 (1993) 619-623.
26. L.A. Rastrigin, R.H. Erenstein, Method of Collective Recognition, Energoizdat, Moscow, 1982 (in Russian).
27. E. Alpaydin, M.I. Jordan, Local linear perceptrons for classification, IEEE Trans. Neural Networks 7 (3) (1996) 788-792.
28. R.P.W. Duin, D.M.J. Tax, Classifier-conditional posterior probabilities in: A. Amin, D. Dori, P. Pudil, H. Freeman (Eds.), Advances in Pattern Recognition, Lecture Notes in Computer Science, Vol. 1451, Springer, Berlin, 1998, pp. 611-619.
29. S.B. Cho, J.H. Kim, Multiple network fusion using fuzzy logic, IEEE Trans. Neural Networks 6 (1995) 497-501.
30. M. Grabisch, J.-M. Nicolas, Classification by fuzzy integral, Fuzzy Sets and Systems 65 (1994) 255-271.
31. D. Wang, J.M. Keller, C.A. Carson, K.K. McAdoo-Edwards, C.W. Bailey, Use of fuzzy-logic-inspired features to improve bacterial recognition through classifier fusion, IEEE Trans. Systems Man Cybernet. 28B (4) (1998) 583-591.
32. Y. Lu, Knowledge integration in a multiple classifier system, Appl. Intell. 6 (1996) 75-86.
33. R.O. Duda, P.E. Hart, Pattern Classification and Scene Analysis, Wiley, New York, 1973.
34. J.C. Bezdek, J.M Keller, R. Krishnapuram, N.R. Pal, Fuzzy Models and Algorithms for Pattern Recognition and Image Processing, Kluwer, Dordrecht, 1999, in Press.
35. L. Lam, C.Y. Suen, Application of majority voting to pattern recognition: an analysis of its behavior and performance, IEEE Trans. Systems Man Cybernet. 27 (5) (1997) 553-568.
36. Y.S. Huang, C.Y. Suen, A method of combining multiple experts for the recognition of unconstrained handwritten numerals, IEEE Trans. Pattern Anal. Mach. Intell. 17 (1995) 90-93.
37. L.I. Kuncheva, An application of OWA operators to the aggregation of multiple classification decisions, in: R.R. Yager, J. Kacprzyk (Eds.), The Ordered Weighted Averaging Operators. Theory and Applications, Kluwer, Dordrecht, USA, 1997, pp. 330-343.
38. M. van Breukelen, R.P.W. Duin, D.M.J. Tax, J.E. den Hartog, Combining classifiers for the recognition of handwritten digits, Proceedings of the First IAPR TC1 Workshop on Statistical Techniques in Pattern Recognition, Prague, Czech Republic, 1997, pp. 13-18.
39. L.I. Kuncheva, On combining multiple classifiers, Proceedings of the Seventh International Conference on Information Processing and Management of Uncertainty (IPMU'98), Paris, France, 1998, pp. 1890-1891.
40. D.M.J. Tax, R.P.W. Duin, M. ban Breukelen, Comparison between product and mean classifier combination rules, Proceedings of the Workshop on Statistical Pattern Recognition, Prague, Czech, 1997.
41. S. Hashem, B. Schmeiser, Y. Yih, Optimal linear combinations of neural networks: an overview, IEEE International Conference on Neural Networks, Orlando, FL, 1994, pp. 1507-1512.
42. S. Hashem, Optimal linear combinations of neural networks, Neural Networks 10 (4) (1997) 599-614.
43. V. Tresp, M. Taniguchi, Combining estimators using non-constant weighting functions, in: G. Tesauro, D.S. Touretzky, T.K. Leen (Eds.), Advances in Neural Information Processing Systems, Vol. 7, MIT Press, Cambridge, MA, 1995.
44. Y.S. Huang, C.Y. Suen, A method of combining multiple classifiers -a neural network approach, Proceedings of the 12th International Conference on Pattern Recognition, Jerusalem, Israel, 1994, pp. 473-475.
45. D. Dubois, H. Prade, Fuzzy Sets and Systems: Theory and Applications, Academic Press, NY, 1980.
46. R.F. Bordley, A multiplicative formula for aggregating probability assessments, Management Sci. 28 (1982) 1137-1148.
47. J.A. Anderson, Logistic discrimination, in: P.R. Krishnaiah, L.N. Kanal (Eds.), Classification, Pattern Recognition and Reduction of Dimensionality, Handbook of Statistics, Vol. 2, North-Holland, Amsterdam, 1982, pp. 169-191.
48. S. Raudys, R.P.W. Duin, On expected classification error of the fisher linear classifier with pseudo-inverse covariance matrix, Pattern Recognition Lett. 19 (5-6) (1998) 385-392.
49. R.P.W. Duin, PRTOOLS (Version 2), A Matlab toolbox for pattern recognition, Pattern Recognition Group, Delft University of Technology, June 1997.
50. L.I. Kuncheva, R.K. Kounchev, R.Z. Zlatev, Aggregation of multiple classification decisions by fuzzy templates, Proceedings of the Third European Congress on Intelligent Technologies and Soft Computing EUFIT'95, Aachen, Germany, August 1995, pp. 1470-1474.