An Efficient Partition of Training Data Set Improves Speed and Accuracy of Cascade-correlation Algorithm

摘要

This study extends an application of efficient partition algorithm (EPA) for artificial neural network ensemble trained according to Cascade Correlation Algorithm. We show that EPA allows to decrease the number of cases in learning and validated data sets. The predictive ability of the ensemble calculated using the whole data set is not affected and in some cases it is even improved. It is shown that a distribution of cases selected by this method is proportional to the second derivative of the analyzed function.