An Improved Self-Structuring Neural Network

Abstract

Creating a neural network based classification model is traditionally accomplished using the trial and error technique. However, the trial and error structuring method nornally suffers from several difficulties including overtraining. In this article, a new algorithm that simplifies structuring neural network classification models has been proposed. It aims at creating a large structure to derive classifiers from the training dataset that have generally good predictive accuracy performance on domain applications. The proposed algorithm tunes crucial NN model thresholds during the training phase in order to cope with dynamic behavior of the learning process. This indeed may reduce the chance of overfitting the training dataset or early convergence of the model. Several experiments using our algorithm as well as other classification algorithms, have been conducted against a number of datasets from University of California Irvine (UCI) repository. The experiments’ are performed to assess the pros and cons of our proposed NN method. The derived results show that our algorithm outperformed the compared classification algorithms with respect to several performance measures.