An efficient MDL-based construction of RBF networks
Neural Networks
A global learing algorithm for a RBF network
Neural Networks
Using Model Trees for Classification
Machine Learning
MultiBoosting: A Technique for Combining Boosting and Wagging
Machine Learning
The constraint based decomposition (CBD) training architecture
Neural Networks
Generating Accurate Rule Sets Without Global Optimization
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Evolutionary ensembles with negative correlation learning
IEEE Transactions on Evolutionary Computation
Inducing oblique decision trees with evolutionary algorithms
IEEE Transactions on Evolutionary Computation
The balance between proximity and diversity in multiobjective evolutionary algorithms
IEEE Transactions on Evolutionary Computation
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A self-optimizing approach for complex classifications is proposed in this paper to construct dynamical radial basis function neural network (RBFNN) models based on a specially designed genetic algorithm (GA). The algorithm adopts a matrix-form mixed encoding and specifically designed genetic operators to optimize the decayed-radius selected clustering (DRSC) process by co-evolving all of the parameters of the network's layout. The individual fitness is evaluated as a multi-objective optimization task and the weights between the hidden layer and the output layer are calculated by the pseudo-inverse algorithm. Experimental results on eight UCI datasets show that the GA-RBFNN can produce a higher accuracy of classification with a much simpler network structure and outperform those models of neural network based on other training methods.