Abstract
We describe in this paper a new method for response integration in ensemble neural networks with Type-1 Fuzzy Logic and Type-2 Fuzzy Logic using Genetic Algorithms (GA’s) for optimization. In this paper we consider pattern recognition with ensemble neural networks for the case of fingerprints. An ensemble neural network of three modules is used. Each module is a local expert on person recognition based on their biometric measure (Pattern recognition for fingerprints). The Response Integration method of the ensemble neural networks has the goal of combining the responses of the modules to improve the recognition rate of the individual modules. Using GA’s to optimize the Membership Functions of The Type-1 Fuzzy System and Type-2 Fuzzy System we can improve the results of the fuzzy systems. We show in this paper the results of a type-2 approach for response integration that improves performance over the type-1 logic approaches.
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References
Castro, J.R., Castillo, O., Melin, P., Martinez, L.G., Escobar, S., Camacho, I.: Building Fuzzy Inference Systems with Interval Type-2 Fuzzy Logic Toolbox, 1st edn. Number 1 in Studies in Fuzziness and Soft Computing, vol. 6, pp. 53–62. Springer, Germany (2007)
Castro, J.R., Castillo, O., Melin, P.: An Interval Type-2 Fuzzy Logic Toolbox for Control Applications. In: Proc. FUZZ-IEEE 2007 (2007)
Chang, S., Greenberg, S.: Fuzzy Measures and Integrals: Theory and Applications, pp. 415–434. Physica-Verlag, NY (2003)
Cunningham, P.: Overfitting and Diversity in Classification Ensembles based on Feature Selection, TCD Computer Science Technical Report, TCD-CS-2000-07.
Grabisch, M., Murofushi, T., Sugeno, M.: Fuzzy Measures and Integrals: Theory and Applications, pp. 348–373. Physica-Verlag, NY (1989)
Grabisch, M.: A new algorithm for identifying fuzzy measures and its application to pattern recognition. In: Proc. of 4th IEEE Int. Conf. on Fuzzy Systems, Yokohama, Japan, pp. 145–150 (1995)
Gutta, S., Huang, J., Takacs, B., Wechsler H.: Face Recognition Using Ensembles of Netrworks. In: 13th International Conference on Pattern Recognition (ICPR 1996), Vienna, Austria, vol. 4, p. 50 (1996)
Maio, D., Maltoni, D., Cappelli, R., Wayman, J.L., Jain, A.K.: FVC 2004: Third Fingerprint Verification Competition. In: Zhang, D., Jain, A.K. (eds.) ICBA 2004. LNCS, vol. 3072, pp. 1–7. Springer, Heidelberg (2004)
Maltoni, D., Maio, D., Jain, A.K., Prabhakar, S.: The full FVC2000 and FVC2002 databases are available in the DVD included. In: Handbook of Fingerprint Recognition, Springer, New York (2003)
MATLAB Trade Marks, by the MathWorks, Inc. © (1994-2007)
Mostafa Abd Allah, M.: Artificial Neural Networks Fingerprints Authentication with Clusters Algorithm. Informatica 29, 303–307 (2005)
Melin, P., Castillo, O.: Hybrid Intelligent Systems for Pattern Recognition Using Soft Computing. Springer, Heidelberg (2005)
Melin, P., Castillo, O.: Hybrid Intelligent Systems for Pattern Recognition using Soft Computing. Springer, Berlin (2005)
Melín, P., González, F., MartínezG.: Pattern Recognition Using Modular Neural Networks and Genetic Algorithms. In: IC-AI 2004, pp 77–83 (2004)
Melín, P., Mancilla, A., Lopez, M., Solano, D., Soto, M., Castillo, O.: Pattern Recognition for Industrial Security using the Fuzzy Sugeno Integral and Modular Neural Networks. In: WSC11 11th Online World Conference on Soft Computing in Industrial Applications (September 18- October 6) (2006)
Melín, P., Urias, J., Solano, D., Soto, M., Lopez, M., Castillo, O.: Voice Recognition with Neural Networks, Type-2 Fuzzy Logic and Genetic Algorithms. Engineering Letters 13(2), 108–116 (2006)
Mendoza, O., Melin, P., Licea, G.: Modular Neural Networks and Type-2 Fuzzy Logic for Face Recognition. In: Reformat, M. (ed.) Proceedings of NAFIPS 2007, San Diego, vol. 1 (June 2007); CD Rom
Nemmour, H., Chibani, Y.: Neural Network Combination by Fuzzy Integral for Robust Change Detection in Remotely Sensed Imagery. EURASIP Journal on Applied Signal Processing 14, 2187–2195 (2005)
Opitz, D.W.: Feature Selection for Ensembles. In: Sixteenth National Conference on Artificial/ Intelligence (AAAI), Orlando, FL, pp. 379–384 (1999)
Opitz, D., Maclin, R.: Popular Ensemble Methods: An Empirical Study. Journal of Artificial Intelligence Research 11, 169–198 (1999)
Opitz, D.W., Shavlik, J.W.: Generating accurate and diverse members of a neural network ensemble. In: Touretzky, D.S., Mozer, M., Hasselmo, M. (eds.) Advances in Neural Information Processing Systems, vol. 8, pp. 535–541. MIT Press, Cambridge (1996)
Sharkey, A.C.: Modularity, combining and artificial neural nets. Connection Science 8, 299–313 (1996)
Sharkey, A.: On combining artificial neural nets. Connection Science 8, 299–313 (1996)
Urias, J., Solano, D., Soto, M., Lopez, M., Melín, P.: Type-2 Fuzzy Logic as a Method of Response Integration in Modular Neural Networks. In: IC-AI 2006, pp. 584-590 (2006)
Zadeh, L.A.: Fuzzy Logic. Computer 1(4), 83–93 (1998)
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Lopez, M., Melin, P., Castillo, O. (2008). Optimization of Response Integration with Fuzzy Logic in Ensemble Neural Networks Using Genetic Algorithms. In: Castillo, O., Melin, P., Kacprzyk, J., Pedrycz, W. (eds) Soft Computing for Hybrid Intelligent Systems. Studies in Computational Intelligence, vol 154. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-70812-4_8
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