An Enhancement of Generalization Ability in Cascade Correlation Algorithm by Avoidance of Overfitting/Overtraining Problem

摘要

The current study investigates a method for avoidance of an overfitting/overtraining problem in Artificial Neural Network (ANN) based on a combination of two algorithms: Early Stopping and Ensemble averaging (ESE). We show that ESE provides an improvement of the prediction ability of ANN trained according to Cascade Correlation Algorithm. A simple algorithm to estimate the generalization ability of the method according to the Leave-One-Out technique is proposed and discussed. In the accompanying paper the problem of optimal selection of training cases is considered for accelerated learning of the ESE method.