Feed-forward neural networks and feature construction with correlation information: an integrated framework

Abstract

Feature construction has been shown to be an useful technique to improve the efficiency of extracting information from raw data. We develop a modified feature construction algorithm, using correlation information among the initial set of features, and study its performance. Feed-forward neural networks, using the back-propagation algorithm to learn, have been shown to have excellent properties while plagued with the problem of time needed to learn concepts. We alleviate this inherent problem with the back-propagation algorithm using data pre-processed by the proposed feature construction algorithm. Initial results suggest that this methodology can be beneficially used along with other means of improving the learning performance in feed-forward neural networks.