AHP-Based Classifier Combination

László Felföld, András Kocsor


Classifier combinations are effective techniques for difficult pattern recognition problems such as speech recognition where the combination of differently trained classifiers can produce a more robust phoneme classification on noisy datasets. In this paper we investigate traditional linear combination schemes (e.g. arithmetic mean and least squares methods), and propose a new combiner based on the Analytic Hierarchy Process (AHP), a method frequently applied in mathematical psychology and multi-criteria decision making. In addition, we experimentally compare the applicability of these linear combination schemes using neural network classifiers on a speech recognition framework and two test sets from the UCI repository.


  1. Anil K. Jain, Robert P. W. Duin, and Jianchang Mao. Statistical pattern recognition: A review. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(1):4-37, 2000.
  2. V. N. Vapnik. Statistical Learning Theory. John Wiley and Son, 1998.
  3. R. O. Duda, P. E. Hart, and D. G. Stork. Pattern Classification. John Wiley and Son, New York, 2001.
  4. L. Xu, A. Krzyzak, and C.Y. Suen. Method of combining multiple classifiers and their application to handwritten numeral recognition. IEEE Trans. on SMC, 22(3):418-435, 1992.
  5. K. Tumer and J. Ghosh. Analysis of decision boundaries in linearly combined neural classifiers. Pattern Recognition, 29:341-348, 1996.
  6. F. Roli and G. Fumera. Analysis of linear and order statistics combiners for fusion of imbalanced classifiers. In 3rd Int. Workshop on Multiple Classifier Systems (MCS 2002), Cagliari, Italy, June 2002. Springer-Verlag, LNCS.
  7. M. P. Perrone and L. N. Cooper. When networks disagree: Ensemble methods for hybrid neural networks. In R. J. Mammone, editor, Neural Networks for Speech and Image Processing, pages 126-142. Chapman-Hall, 1993.
  8. T. L. Saaty. The Analytic Hierarchy Process. McGraw-Hill, New York, 1980.
  9. A. Kocsor and L. T óth. Kernel-based feature extraction with a speech technology application. In IEEE TRANS. ON SIGNAL PROCOCESSING, 2003.
  10. G. Fumera and F. Roli. Linear combiners for classifier fusion: Some theoretical and experimental results. In 4th Int. Workshop on Multiple Classifier Systems (MCS 2003), Guildford, UK, January 2003. Springer-Verlag, LNCS.
  11. Leo Breiman. Bagging predictors. Machine Learning, 24(2):123-140, 1996.
  12. Freund. Boosting a weak learning algorithm by majority. In Proceedings of the Workshop on Computational Learning Theory (COLT 1990). Morgan Kaufmann Publishers, 1990.

Paper Citation

in Harvard Style

Felföld L. and Kocsor A. (2004). AHP-Based Classifier Combination . In Proceedings of the 4th International Workshop on Pattern Recognition in Information Systems - Volume 1: PRIS, (ICEIS 2004) ISBN 972-8865-01-5, pages 45-58. DOI: 10.5220/0002680200450058

in Bibtex Style

author={László Felföld and András Kocsor},
title={AHP-Based Classifier Combination},
booktitle={Proceedings of the 4th International Workshop on Pattern Recognition in Information Systems - Volume 1: PRIS, (ICEIS 2004)},

in EndNote Style

JO - Proceedings of the 4th International Workshop on Pattern Recognition in Information Systems - Volume 1: PRIS, (ICEIS 2004)
TI - AHP-Based Classifier Combination
SN - 972-8865-01-5
AU - Felföld L.
AU - Kocsor A.
PY - 2004
SP - 45
EP - 58
DO - 10.5220/0002680200450058