Abstract
This article presents a new learning algorithm, CO-RBFNN, for complex classifications, which attempts to construct the radial basis function neural network (RBFNN) models by using a cooperative coevolutionary algorithm (Co-CEA). The Co-CEA utilizes a divide-and-cooperative mechanism by which subpopulations are coevolved in separate populations of evolutionary algorithms executing in parallel. A modified K-means method is employed to divide the initial hidden nodes into modules that are represented as subpopulation of the Co-CEA. Collaborations among the modules are formed to obtain complete solutions. The algorithm adopts a matrix-form mixed encoding to represent the RBFNN hidden layer structure, the optimum of which is achieved by coevolving all parameters. Experimental results on eight UCI datasets illustrate that CO-RBFNN is able to produce a higher accuracy of classification with a much simpler network structure in fewer evolutionary trials when compared with other alternative standard algorithms.
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References
Blanco, A., Delgado, M., Pegalajar, M.C.: A Real-Coded Genetic Algorithm for Training Recurrent Neural Networks. Neural Networks 14, 93–105 (2001)
Sarimveis, H., Alexandridis, A., et al.: A New Algorithm for Developing Dynamic Radial Basis Function Neural Network Models Based on Genetic Algorithms. Computers and Chemical Engineering 28, 209–217 (2004)
Potter, M., De Jong, K.: Cooperative Coevolution: an Architecture for Evolving Coadapted Subcomponents. Evolutionary Computation 8, 1–29 (2000)
GarcÃa-Pedrajas, N., Hervás-MartÃnez, C., Ortiz-Boyer, D.: Cooperative Coevolution of Artificial Neural Network Ensembles for Pattern Classification. IEEE transactions on evolutionary computation 9, 271–302 (2005)
Casasent, D., Chen, X.W.: Radial Basis Function Neural Networks for Nonlinear Fisher Discrimination and Neyman-Pearson Classification. Neural Networks 16, 529–535 (2003)
Berthold, M.R., Diamond, J.: Boosting the Performance of RBF Networks with Dynamic Decay Adjustment. In: Tesauro, G., Touretzky, D.S., Leen, T.K. (eds.) Advances in Neural Information Processing Systems, vol. 7, Denver, Colorado, pp. 512–528. MIT Press, Cambridge (1995)
Bosman, P.A.N., Thierens, D.: The Balance between Proximity and Diversity in Multiobjective Evolutionary Algorithms. IEEE Transactions on Evolutionary Computation 7, 174–188 (2003)
Liu, Y., Yao, X., Higuchi, T.: Evolutionary Ensembles with Negative Correlation Learning. IEEE Transactions on Evolutionary Computation 4, 380–387 (2000)
Li, M.Q., Kou, J.S., et al.: The Basic Theories and Applications in GA. Science Press, Beijing (2002)
Tian, J., Li, M.Q., Chen, F.Z.: GA-RBFNN Learning Algorithm for Complex Classifications. Journal of Systems Engineering 21, 163–170 (2006)
Frank, E., Wang, Y., Inglis, S., et al.: Using Model Trees for Classification. Machine Learning 32, 63–76 (1998)
Merz, C.J.: Using Correspondence Analysis to Combine Classifiers. Machine Learning 36, 33–58 (1999)
Cantú-Paz, E., Kamath, C.: Inducing oblique Decision Trees with Evolutionary Algorithms. IEEE Transactions on Evolutionary Computation 7, 54–68 (2003)
Friedman, J., Hastie, T., Tibshirani, R.: Additive Logistic Regression: A Statistical View of Boosting. Annals of Statistics 28, 337–407 (2000)
Draghici, S.: The Constraint Based Decomposition (CBD) Training Architecture. Neural Networks 14, 527–550 (2001)
Webb, G.I.: Multiboosting: A Technique for Combining Boosting and Wagging. Machine Learning 40, 159–196 (2000)
Yang, J., Parekh, R., Honavar, V.: DistAI: an Inter-pattern Distance-based Constructive Learning Algorithm. Intelligent Data Analysis 3, 55–73 (1999)
Frank, E., Witten, I.H.: Generating Accurate Rule Sets Without Global Optimization. In: Shavlik, J.W. (ed.) Proc. of the Fifteenth International Conference on Machine Learning, pp. 144–151. Morgan Kaufmann, San Francisco (1998)
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Tian, J., Li, M., Chen, F. (2007). A Cooperative Coevolution Algorithm of RBFNN for Classification. In: Zhou, ZH., Li, H., Yang, Q. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2007. Lecture Notes in Computer Science(), vol 4426. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71701-0_89
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DOI: https://doi.org/10.1007/978-3-540-71701-0_89
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