Key concepts in model selection: performance and generalizability
Journal of Mathematical Psychology
Sensitivity analysis of multilayer perceptron to input and weight perturbations
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
The selection of weight accuracies for Madalines
IEEE Transactions on Neural Networks
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For a well-trained radial basis function neural network, this paper proposes a novel input stochastic sensitivity definition and gives its computational formula assuming the inputs are modelled by normal distribution random variables. Based on this formula, one can calculate the magnitude of sensitivity for each input (i.e. feature), which indicates the degree of importance of input to the output of neural network. When there are redundant inputs in the training set, one always wants to remove those redundant features to avoid a large network. This paper shows that removing redundant features or selecting significant features can be completed by choosing features with sensitivity over a predefined threshold. Numerical experiment shows that the new approach to feature selection performs well.