Decision Combination in Multiple Classifier Systems

IEEE Transactions on Pattern Analysis and Machine Intelligence

Volumn 16, Issue 1, 1994, Pages 66-75

  Ho, Tin kam ^a   Hull, Jonathan J ^a   Srihari, Sargur N ^a  

^a UNIVERSITY AT BUFFALO   (United States)

Author keywords

character recognition; classifier combination; Decision combination; multiple classifier systems; pattern reeog nition

Indexed keywords

CHARACTER RECOGNITION; CLASSIFICATION (OF INFORMATION); COMPUTATIONAL METHODS; DATA PROCESSING; DECISION THEORY; EQUIVALENCE CLASSES; PATTERN RECOGNITION; REGRESSION ANALYSIS;

CLASSIFIER COMBINATION; DECISION COMBINATION; MULTIPLE CLASSIFIER SYSTEMS;

PATTERN RECOGNITION SYSTEMS;

EID: 0028259890     PISSN: 01628828     EISSN: None     Source Type: Journal    
DOI: 10.1109/34.273716     Document Type: Article

References (34)

1. A. Agresti, Categorical Data Analysis. New York: Wiley, 1990.
2. J. A. Anderson, “Regression and ordered categorical variables,” J. Roy. Statist. Soc., Ser. B, vol. 46, no. 1, pp. 1-30, 1984.
3. H. S. Baird, H. P. Graf, L. D. Jacket, and W. E. Hubbard, “A VLSI architecture for binary image classification,” in From Pixels to Features, J. C. Simon, Ed. Amsterdam: North-Holland, 1989, pp. 275-286.
4. D. Black, The Theory of Committees and Elections, 2nd ed. London: Cambridge University Press, 1958, 1963.
5. R. Bradford and T. Nartker, “Error correlation in contemporary OCR systems,” in Proc. 1st Int. Conf. Document Analysis and Recognition, Saint-Molo, France, 1991, pp. 516-523.
6. D. R. Cox and E. J. Snell, Analysis of Binary Data, ed. Burlington, VT: Chapman and Hall, 1989.
7. R. O. Duda and P. E. Hart, Pattern Classification and Scene Analysis. New York: Addison-Wesley, 1973.
8. B. Efron, “The efficiency of logistic regression compared to normal discriminant analysis,” J. Amer. Statist. Ass., vol. 70, no. 352, pp. 892-898, Dec. 1975.
9. R. M. Haralick, “The table look-up rule,” Commun. Statist.—Theory and Methods, vol. A5, no. 12, pp. 1163-1191, 1976.
10. T. Hastie, personal communication, 1992.
11. T. P. Hettmansperger, Statistical Inference Based on Ranks. New York: Wiley, 1984.
12. T. K. Ho, J. J. Hull, and S. N. Srihari, “Combination of structural classifiers,” in Pre-Proc. IAPR Syntactic and Structural Pattern Recognition Workshop, Murray Hill, NJ, June 1990, pp. 123-136.
13. T. K. Ho, A Theory of Multiple Classifier Systems and Its Application to Visual Word Recognition, Ph.D. dissertation, Dept. of Computer Science, SUNY at Buffalo, 1992.
14. T. K. Ho, J. J. Hull and S. N. Srihari, “A regression approach to combination of decisions by multiple character recognition algorithms,” in SPIE Proc. Vol. 1661, Machine Vision Applications in Character Recognition and Industrial Inspection, San Jose, CA, Feb. 10-12, 1992.
15. T. K. Ho, J. J. Hull and S. N. Srihari, “On multiple classifier systems for pattern recognition,” in Proc. 11th Int. Conf. Pattern Recognition, The Hague, Netherlands, Aug. 30-Sept. 3, 1992, pp. 84-87.
16. T. K. Ho, J. J. Hull and S. N. Srihari, “A word shape analysis approach to lexicon based word recognition,” Patt. Recogn. Lett., vol. 13, pp. 821-826, 1992.
17. T. K. Ho, J. J. Hull and S. N. Srihari, “Combination of decisions by multiple classifiers,” in Structured Document Image Analysis, H. Baird, H. Bunke, and K. Yamamoto, Eds. New York: Springer-Verlag, 1992, pp. 188-202.
18. T. K. Ho, J. J. Hull and S. N. Srihari, “A computational model for recognition of multifont word images,” Machine Vision and Applications, vol. 5, pp. 157-168, 1992.
19. D. W. Hosmer and S. Lemeshow, Applied Logistic Regression. New York: Wiley, 1989.
20. J. J. Hull, A. Commike, and T. K. Ho, “Multiple algorithms for handwritten character recognition,” in Proc. 1st Int. Workshop on Frontiers in Handwriting Recognition, Montreal, Apr. 1990, pp. 117-124.
21. F. Kimura and M. Shridhar, “Handwritten numerical recognition based on multiple algorithms,” Patt. Recogn. vol. 24, no. 10, pp. 969-983, 1991.
22. E. M. Kleinberg, “Stochastic discrimination,” Ann. Math. Artificial Intell., vol. 1, pp. 207-239, 1990.
23. E. M. Kleinberg, “The theory of stochastic modeling in pattern recognition,” in preparation.
24. E. M. Kleinberg and T. K. Ho, “Pattern recognition by stochastic modeling,” in Proc. 3rd Int. Workshop on Frontiers in Handwriting Recognition. Buffalo, NY, May 1993, p. 175.
25. J. M. Landwehr, D. Pregibon, and A. C. Shoemaker, “Graphical methods for assessing logistic regression models,” J. Amer. Statist. Ass., vol. 79, no. 385, pp. 61-71, Mar. 1984.
26. S. le Cessie, “Model building techniques for logistic regression, with applications to medical data,” Ph.D dissertation, Univ. of Leiden, The Netherlands, 1991.
27. E. Mandler and J. Schuermann, “Combining the classification results of independent classifiers based on the Dempster/Shafer theory of evidence,” in Pattern Recognition and Artificial Intelligence, E. S. Gelsema and L. N. Kanal, Eds. Amsterdam: North-Holland, 1988, pp. 381-393.
28. V. D. Mazurov, A. I. Krivonogov, and V. L. Kazantsev, “Solving of optimization and identification problems by the committee methods,” Patt. Recogn., vol. 20. no. 4. pp. 371-378, 1987.
29. R. Meddis, Statistics Using Ranks: A Unified Approach. Philadelphia: Basil Blackwell, 1984.
30. C. Nadal, R. Legault, and C. Y. Suen, “Complementary algorithms for the recognition of totally unconstrained handwritten numerals,” in Proc. 10th Int. Conf. Pattern Recognition, Atlantic City, NJ, 1990, pp. 443-449.
31. S. J. Press and S. Wilson, “Choosing between logistic regression and discriminant analysis,” J. Amer. Statist. Ass., vol. 73, no. 364, pp. 699-705, Dec. 1978.
32. SAS Institute Inc., SAS/STAT User's Guide, version 6, 4th ed., vol. 2. Cary, NC: SAS Institute Inc., 1989.
33. K-D. Wernecke, “A coupling procedure for the discrimination of mixed data,” Biometrics, vol. 48, pp. 497-506, June 1992.
34. L. Xu, A. Krzyzak, and C.Y. Suen, “Methods of combining multiple classifiers and their applications to handwriting recognition,” IEEE Trans. Syst., Man, Cybern., vol. SMC-22, no. 3, pp. 418-435, May/June 1992.