Combination, cooperation and selection of classifiers: A state of the art

International Journal of Pattern Recognition and Artificial Intelligence

Volumn 17, Issue 8, 2003, Pages 1303-1324

  Gunes, V ^a   Menard M ^a   Loonis, P ^a   Petit Renaud, S ^b  

^a UNIVERSITÉ DE LA ROCHELLE   (France)

^b UNIVERSITÉ DU MAINE   (France)

Author keywords

Adaptive selection; Combination; Cooperation; Dynamic selection; Ensemble of classifiers; Multiple classifier systems

Indexed keywords

DECISION MAKING; GENETIC ALGORITHMS; LEARNING SYSTEMS; NEURAL NETWORKS;

ADAPTIVE SELECTION; COMBINATION; COOPERATION; DYNAMIC SELECTION; ENSEMBLE OF CLASSIFIERS; MULTIPLE CLASSIFIER SYSTEMS;

PATTERN RECOGNITION;

EID: 0842310660     PISSN: 02180014     EISSN: None     Source Type: Journal    
DOI: 10.1142/S0218001403002897     Document Type: Article

References (71)

1. B. Achermann and H. Bunke, "Combination of classifiers on the decision level for face recognition," Report IAM-96-002, Institut fü Informatik, University of Bern, Switzerland, January 1996.
2. F. M. Alkoot and J. Kittler, "Improving the performance of the product fusion strategy," Proc. ICPR2000, 15th Int. Conf. Pattern Recognition, Barcelona, Spain, September 2000, pp. 164-167.
3. D. Bahler and L. Navarro, "Methods for combining heterogeneous sets of classifiers," 17th Natl, Conf. Artificial Intelligence (AAAI 2000), Workshop on New Research Problems for Machine Learning, 2000.
4. I. Bloch, "Information combination operators for data fusion: a comparative review with classification," IEEE Trans. Syst. Man Cybern. 26, 1 (1996) 52-67.
5. I. Bloch and H. Maître, "Fusion of Image Information under Imprecision," Aggregation and Fusion of Imperfect Information, ed. B. Bouchon-Meunier, Series Studies in Fuzziness, Physica Verlag, Springer, 1997, pp. 189-213.
6. L. Breiman, "Bagging predictors," Mach. Learn. 24, 2 (1996) 123-140.
7. J. Cao, M. Ahmadi and M. Shridhar, "Handwritten numerals with multiple features and multistage classifiers," IEEE Int. J. Circuits Syst. 6 (1996) 323-326.
8. K. Chen, L. Wang and H. Chi, "Methods of combining multiple classifiers with different features and their applications to text-independant speaker identification," Int. J. Pattern Recognition and Artificial Intelligence 11, 3 (1997) 417-445.
9. S. B. Cho and J. H. Kim, "Combining multiple neural networks by fuzzy integral for robust classification," IEEE Trans. Syst. Man Cybern. SMC-25, 2 (1995) 380-384.
10. V. M. Dang, "Classification de données spatiales: modèles probabilistes et critères de partitionnement," Ph.D. Thesis, Université de Compiègne, 1998.
11. A. P. Dempster, "Upper and lower probabilities induced by a multivalued mapping," Ann. Math. Statist. 38 (1967) 325-339.
12. A. P. Dempster, N. M. Laird and D. B. Rubin, "Maximum likelihood estimation from incomplete data via the EM algorithm," J. Roy. Statist. Soc. B39 (1977) 1-38.
13. T. Denœux, "A k-nearest neighbor classification rule based on Dempster-Shafer theory," IEEE Trans. Syst. Man Cybern. 25, 5 (1995) 804-813.
14. T. Denœux, "Analysis of evidence-theoretic decision rules for pattern classification," Patt. Recogn. 30, 7 (1997) 1095-1107.
15. D. Dubois and H. Prade, "A review of fuzzy sets aggregation connectives," Inform. Sci. 36 (1985) 85-121.
16. D. Dubois and H. Prade, "On the unicity of dempster rule of combination," Int. J. Intell. Syst. 1 (1986) 133-142.
17. D. Dubois and H. Prade, Possibility Theory: An Approach to Computerized Processing of Uncertainty, Plenum Press, NY, 1988.
18. J. Franke and E. Mandler, "A comparison of two approaches for combining the votes of cooperating classifiers," Proc. 11th Int. Conf. Pattern Recognition, Vol. 2, The Hague, Netherlands, 1992, pp. 611-614.
19. G. Giacinto and F. Roli, "Adaptive selection of image classifiers," ICIAP'97, 9th Int. Conf. Image Analysis and Processing, Florence, Italy, September 1997, Lecture Notes in Computer Science 1310, Springer, pp. 38-45.
20. G. Giacinto and F. Roli, "Ensembles of neural networks for soft classification of remote-sensing images," Proc. Eur. Symp. Intelligent Techniques, Bari, Italy, March 1997, pp. 166-170.
21. G. Giacinto and F. Roli, "Methods for dynamic classifier selection," ICIAP'99, 10th Int. Conf. Image Analysis and Processing, Venice, Italy, September 1999, pp. 659-664.
22. G. Giacinto and F. Roli, "An approach to the automatic design of multiple classifier systems," Patt. Recogn. Lett. 22 (2001) 25-33.
23. G. Giacinto, F. Roli and G. Fumera, "Design of effective multiple classifier systems by clustering of classifiers," Proc. ICPR2000, 15th Int. Conf Pattern Recognition, Barcelone, Espagne, September 2000, pp. 160-163.
24. A. Giani, "Parallel cooperative classifier systems," Ph.D. Thesis, Université de Pise, Italie, March 1999.
25. B. Gosselin, "Cooperation of multilayer perceptron classifiers," Proc. 8th Works Circuits Systems and Signal Processing, Mierlo, Pays-Bas, 1997, pp. 187-190.
26. V. Gunes, P. Loonis and M. Ménard, "A fuzzy petri net for pattern recognition: application to dynamic classes," Knowl. Inform. Syst. 4, 1 (2002) 112-128.
27. V. Gunes, M. Ménard and P. Loonis, "A multiple classifier system using ambiguity rejection for clustering-classification cooperation," Int. J. Uncert. Fuzz. Knowl. Based Syst. 8, 6 (2000) 747-762.
28. D. Happel and P. Bock, "Overriding the experts: a stacking method for combining marginal classifiers," Proc. 13th Int. FLAIRS Conf., Menlo Park, CA, USA, 2000.
29. R. M. Haralick, "The table look-up rule," Commun. Stat. - Th. Meth. A5, 12 (1976) 1163-1191.
30. T. K. Ho, "A theory of multiple classifier systems and its application to visual word recognition," Ph.D. Thesis, State University of New York at Buffalo, May 1992.
31. T. K. Ho, J. J. Hull and S. N. Srihari, "Decision combination in multiple classifier systems," IEEE Trans. Patt. Anal. Mach. Intell. 16 (1994) 66-75.
32. T. K. Ho, "Random decision forests," Proc. 3rd Int. Conf. Document Analysis and Recognition, Montreal, Canada, August 1995, pp. 278-282.
33. T. K. Ho, "The random subspace method for constructing decision forests," IEEE Trans. Patt. Anal. Mach. Intell. 20, 8 (1998) 832-844.
34. T. S. Huang and C. Y. Suen, "Combination of multiple experts for the recognition of unconstrained handwritten numerals," IEEE Trans. Patt. Anal. Mach. Intell. 17, 1 (1995) 90-94.
35. Y. S. Huang and C. Y. Suen, "A method of combining multiple classifiers - a neural network approach," Proc. 12th Int. Conf. Pattern Recognition and Computer Vision, Jerusalem, 1994, pp. 473-475.
36. A. Jaimes and S. F. Chang, "Automatic selection of visual features and classifiers," IS&T/SPIE Storage and Retrieval for Image and Video Databases VIII, San Jose, CA, USA, January 2000.
37. A. Jaimes and S. F. Chang, "Integrating multiple classifiers in visual objects detectors learned from user input," 4th Asian Conf. Computer Vision (ACCV2000), Taipei, Taiwan, January 2000.
38. F. Kimura and M. Shridar, "Handwritten numerical recognition based on multiple algorithms," Patt. Recogn. 24, 10 (1991) 969-983.
39. L. I. Kuncheva, C. A. Whitaker, C. A. Shipp and R. P. W. Duin, "Is independence good for combining classifiers?" Proc. ICPR2000, 15th Int. Conf. Pattern Recognition, Barcelone, Espagne, September 2000, pp. 168-171.
40. L. Lam, "Classifier combinations: implementations and theoretical issues," Proc. First Int. Work. Multiple Class. Syst., Lecture Notes in Computer Science, 2000, pp. 77-86.
41. L. Lam and C. Y. Suen, "A theoritical analysis of the application of majority voting to pattern recognition," Int. Conf. Pattern Recognition, Jerusalem, 1994, pp. 418-420.
42. P. Loonis and M. Ménard, "Fusion connexionniste et algorithme gntique pour adapter les systémes de reconnaissance des formes á leur environnement," Traitement du Signal, November 1996.
43. S. Mathevet, L. Trassoudaine, P. Checchin and J. Alizon, "Combinaison de segmentations en régions," Traitement du Signal 16, 2 (1999) 93-104.
44. G. J. McLachlan and K. E. Basford, Mixture Models: Inference and Applications to Clustering, Marcel Dekker, NY, July 1988.
45. G. S. Ng and H. Singh, "Data equalisation with evidence combination for pattern recognition," Patt. Recogn. Lett. 19 (1998) 227-235.
46. N. J. Nilsson, Learning Machines, McGraw-Hill, 1965.
47. J. R. Quinlan, "Bagging, boosting, and c4.5," Proc. 13th Nat. Conf. Artificial Intelligence, Portland, Oregon, USA, August 1996, pp. 725-730.
48. G. Rogova, "Combining the results of several neural network classifiers," Neural Networks 7, 5 (1994) 777-781.
49. F. Roll and G. Giacinto, "Design of multiple classifier systems," Hybrid Methods in Pattern Recognition, eds. H. Bunke and A. Kandel, Series on Machine Perception and Artificial Intelligence, World Scientific, 2002.
50. S. L. Salzberg, "On comparing classifiers: a critique of current research and methods," Data Min. Knowl. Discovery 1 (1999) 1-12.
51. G. Shafer, A Mathematical Theory of Evidence, Princeton University Press, Princeton, NJ, 1976.
52. R. E. Shapire, Y. Freund, P. Bartlett and W. S. Lee, "Boosting the margin: a new explanation for the effectiveness of voting methods," Proc. 14th Int. Conf. Machine Learning, 1997.
53. A. J. C. Sharkey, "Multi-net systems," Combining Artificial Neural Nets, Ensemble and Modular Multi-Net Systems, 1999, pp. 1-27.
54. A. J. C. Sharkey and N. E. Sharkey, "The 'test and select' approach to ensemble combination," Proc. First Int. Works. Multiple Classifier Systems, Lecture Notes in Computer Science, 2000, pp. 30-44.
55. P. Smets, "The combination of evidence in the transferable belief model," IEEE Trans. Patt. Anal. Mach. Intell. 12, 5 (1980) 447-458.
56. P. Smets, "Belief functions: the disjuntive rule of combination and the generalized Bayesian theorem," Int. J. Approx. Reason. 9 (1993) 1-35.
57. P. Smets, "Decision analysis using belief functions," Int. J. Approx. Reason, 4 (1994) 391-418.
58. P. Smets and R. Kennes, "The transferable belief model," Artif. Intell. 66 (1994) 191-243.
59. C. Y. Suen, C. Nadal, R. Legault, T. A. Mai and L. Lam, "Computer recognition of unconstrained handwritten numerals," Proc. IEEE 80 (1992) 1162-1180.
60. C. Y. Suen, T. A. Nadal, T. A. Mai, R. Legault and L. Lam, "Recognition of totally unconstrained handwritten numerals based on the concept of multiple experts," Frontiers in Handwriting Recognition, Montreal Corcordia University, 1990, pp. 131-143.
61. K. Turner and J. Ghosh, "Error correlation and error reduction in ensemble classifiers," Connect. Sci. 8 (1996) 385-404.
62. M. Van Breukelen, R. P. W. Duin and D. M. J. Tax, "Combining classifiers for the recognition of handwritten digits," 1st IAPR TC1 Statistical Technniques in Pattern Recognition, Prague, Tchéquie, June 1997, pp. 13-18.
63. A. Verikas, A. Lipnickas, K. Malmqvist, M. Bacauskiene and A. Gelzinis, "Soft combination of neural classifiers: a comparative study," Patt. Recogn. Lett. 20 (1999) 429-444.
64. D. H. Wolpert, "Stacked generalization," Neural Networks 5, 2 (1992) 241-259.
65. K. Woods, W. P. Kegelmeyer and J. R. K. Bowyer, "Combination of multiple classifiers using local accuracy estimates," IEEE Trans. Patt. Anal. Mach. Intell. 19, 4 (1997).
66. L. Xu, A. Krzyzak and C. Y. Suen, "Methods of combining multiple classifiers and their applications to handwriting recognition," IEEE Trans. Syst. Man Cybern. 22, 3 (1992) 418-435.
67. R. R. Yager, "On ordered weighted averaging aggregation operators in multi-criteria decision making," IEEE Trans. Syst. Man Cybern. 18 (1988) 183-190.
68. K. Yu, X. Jiang and H. Bunke, "Combining acoustic and visual classifiers for the recognition of spoken sentences," Proc. ICPR2000, 15th Int. Conf. Pattern Recognition, Barcelona, Spain, September 2000, pp. 491-494.
69. L. A. Zadeh, "Fuzzy sets," Inform. Contr. 8 (1965) 338-353.
70. L. A. Zadeh, "Outline of a new approach to the analysis of complex systems and decision processes," IEEE Trans. Syst. Man Cybern. 3 (1973) 28-44.
71. E. Zahzah, "Contribution à la représentation des connaissances et à leur utilisation pour l'interprétation automatique des images satellites," Ph.D. Thesis, Université Paul Sabatier, Toulouse, September 1992.