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Data splitting is an important step in artificial neural network (ANN) models, which is found in the form of training and testing subsets. In general, a random data splitting method is favored to divide a pool of samples into subsets, without considering the quality of data for the training step of a neural network. The drawback of poor data splitting methods is that they poses ill effects to the performance of the neural network when the data involves complex matrices or multivariate modeling. In order to overcome this drawback, the current paper presents our proposed M-SPXY method. It is based on a modified version of Sample Set Partitioning, which relies on a joint X-y distances (SPXY) method. The proposed method has resulted in better performance, compared to the modified Kennard-Stone (KS) method, using Mahalanobis distances (MDKS). In our experiments, the proposed approach was employed to compare various data splitting methods using data sets from the repository of the University of California in Irvine (UCI), processed through an Extreme Learning Machine (ELM) neural network. Performance was measured in terms of classification accuracy. The results indicate that the classification accuracy of the proposed M-SPXY process is superior to that of the MDKS data splitting method. [ABSTRACT FROM AUTHOR]

Trained radial basis function networks are well-suited for use in extracting rules and explanations because they contain a set of locally tuned units. However, for rule extraction to be useful, these networks must first be pruned to eliminate unnecessary weights. The pruning algorithm cannot search the network exhaustively because of the computational effort involved. It is shown that using multiple pruning methods with smart ordering of the pruning candidates, the number of weights in a radial basis function network can be reduced to a small fraction of the original number. The complexity of the pruning algorithm is quadratic (instead of exponential) in the number of network weights. Pruning performance is shown using a variety of benchmark problems from the University of California, Irvine machine learning database. [ABSTRACT FROM AUTHOR]