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Parameter and architectural selection for multiple layered perceptron (MLP) classifiers involve a number of heuristic design procedures. The aim in the design process of such classifiers is to achieve maximum generalization and avoid over-fitting of the training data. It has been the objective of this study to develop a symbolic prediction model to calculate the point at which training should cease for a given neural network (NN) based 12-lead ECG classifier to ensure maximum generalization. This prediction model has been obtained by means of genetic programming (GP), where a GP individual has been evolved to generate a symbolic model that predicts the optimal number of training epochs for three different ECG myocardial infarction classifiers: Anterior myocardial infarction (AMI), inferior myocardial infarction (IMI), and combined myocardial infarction (CMI). The GP model demonstrated to be a very accurate method showing no significant differences between the optimal number of epoch values and the predicted values for both train and test data sets for the three aforementioned pathologies.
Date of Conference: 2001