Rank and response combination from confusion matrix data

Information Fusion

Volumn 2, Issue 2, 2001, Pages 113-120

  Parker, J R ^a  

^a UNIVERSITY OF CALGARY   (Canada)

Author keywords

Classifier combination; Data fusion

Indexed keywords

EID: 0041331721     PISSN: 15662535     EISSN: None     Source Type: Journal    
DOI: 10.1016/S1566-2535(01)00030-6     Document Type: Article

References (19)

1. G.A. Baraghimian, A. Klinger, Preference voting for sensor fusion, in: Proceedings of SPIE Sensor Fusion III, Orlando, FL, (1990) 19-20.
2. Jean-Charles de Borda, Memoire sur les elections au scrutin, Histoire de l'Academie Royale des Sciences, Paris (1781).
3. Marquis de Condorcet, Essai sur l'application de l'analyse a la probabilite des decisions rendues a la pluralite des voix (Essay on the Application of Analysis to the Probability of Majority Decisions), Paris, 1785.
4. Cook W.D., Kress M. Ordinal Information and Preference Structures. 1992;Prentice-Hall, Englewood Cliffs, NJ.
5. B.V. Dasarathy, Asymmetric fusion strategies for target detection in multisensor environments, in: Proceedings of the SPIE, Sensor Fusion: Architecture, Algorithms, and Applications, 3067 (1997) 26-37.
6. P. Fishburn, Preference structures and their numerical representations, in: Proceedings of ORDAL'96, Ottawa, 1996, August 5-9.
7. Ho T.K., Hull J.J., Srihari S.N. Decision combination in multiple classifier systems. IEEE Transactions on Pattern Analysis and Machine Intelligence. 16(1):1994;66-75.
8. Holte R.C. Very simple classification rules perform well on most commonly used data sets. Machine Learning. 11:1993;63-90.
9. Kimura F., Shridhar M. Handwritten numeral recognition based on multiple algorithms. Pattern Recognition. 24(10):1991;969-983.
10. J. Kittler, M. Hatef, R.P. Duin, Combining classifiers, in: Proceedings of the ICPR, 1996, pp. 897-901.
11. J.R. Parker, Voting methods for multiple autonomous agents, in: Proceedings of ANZIIS'96, Perth, Australia, 1996.
12. J.R. Parker, Evaluating Classifier Combination Using Simulated Classifiers, Department of Computer Science, University of Calgary, Research Report #2000/659/11.
13. Quinlan J.R. C4.5: Program for Machine Learning. 1993;Morgan Kaufmann, San Francisco.
14. Straffin P.D. Jr. Topics in the Theory of Voting. 1980;Birkhäuser, Boston.
15. K. Tumer, J. Ghosh, Classifier combining through trimmed means and order statistics, in: Proceedings of the international Joint Conference on Neural Networks, May 1998, Anchorage, AL, pp. 757-762.
16. K. Tumer, J. Ghosh, Order statistics combiners for neural classifiers, in: Proceedings of the World Congress on Neural Networks, July 1995, Washington, DC, pp. I:31-34.
17. Witten I.H., Frank E. Practical Machine Learning: Tools and Techniques with Java Implementations. 2000;Morgan Kaufmann, San Francisco.
18. Wolpert D.H. Stacked generalization. Neural Networks. 5:1992;241-259.
19. Xu L., Krzyzak A., Suen C.Y. Methods of combining multiple classifiers and their applications to handwriting recognition. IEEE Transactions of SMC. 22(3):1992;418-435.