Abstract
In this paper we present a new architecture for combining classifiers. This approach integrates learning into the voting scheme used to aggregate individual classifiers decisions. This overcomes the drawbacks of having static voting techniques. The focus of this work is to make the decision fusion a more adaptive process. This approach makes use of feature detectors responsible for gathering information about the input to perform adaptive decision aggregation. Test results show improvement in the overall classification rates over any individual classifier, as well as different static classifier-combining schemes.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Kittler J., Hatef M., Robert D., Matas J. (1998) On Combining Classifiers. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(3), pp. 226–239
Auda, G., Kamel M. (1998) Modular Neural Network Classifiers: A Compartive Study. Journal of Intelligent and Robotic Systems, 21, pp. 117–129.
Lam, L., Suen, C. (1995), Optimal combination of pattern classifiers, Pattern recognition Letters, 16, pp. 945–954.
Munro, P., Parmanto, B. (1997), Competition Among Networks Improves Committee Performance, In: Mozer, M., Jordon, M., Petsche, T. (Eds.), Advances in Neural Information Processing Systems 9, MIT Press, Cambridge, pp. 592–598.
Hashem, S. (1997), Optimal Linear Combinations of Neural Networks, Neural Networks 10(4), pp. 599–614.
Ho, T., Hull, J., Srihari, S. (1994), Decision Combination in Multiple Classifier Systems, IEEE Transactions on Pattern Analysis and Machine Intelligence, 16(1), pp. 66–75.
Rogova, G. (1994). Combining the Results of Several Neural Network Classifiers. Neural Networks 7(5), pp. 777–781.
Gader, P., Mohamed, M, Keller, J. (1996), Fusion of Handwritten Word Classifiers, Pattern Recognition Letters, 17, pp. 577–584.
Woods, K., Kegelmeyer, W., Bowyer, K. (1997), Combining of Multiple Classifiers Using Local Accuracy Estimates, IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(4), pp. 405–410.
Huang, Y., Suen, C. (1995), A Method of Combining Multiple Experts for the Recognition of Unconstrained Handwritten Numerals, IEEE Transactions on Pattern Analysis and Machine Intelligence, 17(1), pp. 90–94.
Wolpert, D. (1992), Stacked Generalization, Neural Networks, 5, pp. 241–259.
Jacobs, R., Jordan, S., Nowlan, M., Hinton, G. (1991), Adaptive Mixture of Local Experts, Neural Computations 3, pp. 78–88.
Auda, G., Kamel M. (1997). CMNN: Cooperative Modular Neural Networks for Pattern Recognition. Pattern Recognition Letters, 18, pp. 1391–1398.
Hodge, L., Auda, G., Kamel, M. (1999), Learning Decision Fusion in Cooperative Modular Neural Networks, In: Proceedings of the International Joint Conference on Neural Networks, Washington D.C..
Dasarthy, B. (1994). Decision Fusion. IEEE Computer Society Press.
Drawish, A., Auda, G. (1994). New Composite Feature Vector for Arabic Handwritten Signature Recognition. In: Proceedings of the 1994 Intern ational Conference on Acoustics, Speech and Signal Processing, Australia.
Auda, G., Kamel, M., Raafat, H. (1995). Voting Schemes for Cooperative Neural Network Classifiers. In: Proceedings of the 1995 International Conference on Neural Networks, Perth, Australia, pp. 1240–1243.
Topping, J. (1962). Errors of Observation and their Treatment. Chapman and Hall Ltd., London.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2001 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wanas, N.M., Kamel, M.S. (2001). Feature Based Decision Fusion. In: Singh, S., Murshed, N., Kropatsch, W. (eds) Advances in Pattern Recognition — ICAPR 2001. ICAPR 2001. Lecture Notes in Computer Science, vol 2013. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44732-6_18
Download citation
DOI: https://doi.org/10.1007/3-540-44732-6_18
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-41767-5
Online ISBN: 978-3-540-44732-0
eBook Packages: Springer Book Archive