Article ID: | iaor1999369 |
Country: | Netherlands |
Volume: | 93 |
Issue: | 2 |
Start Page Number: | 418 |
End Page Number: | 427 |
Publication Date: | Sep 1996 |
Journal: | European Journal of Operational Research |
Authors: | Piramuthu Selwyn |
Feature construction has been shown to be a 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.