Comparison between entropy net and decision tree classifiers
Ishwar K. Sethi, Maximilian Otten · 1990
A class of hierarchical feedforward layered neural networks, called entropy nets, has been proposed to overcome the difficulty of the credit assignment problem and to introduce the self-configurable capability in the design and training of artificial neural networks. These networks are based on mapping binary decision trees into a three-layer structure. The authors report the results of an experimental study comparing the classification performance of the decision tree and the entropy-net classifiers. In addition to the consistently better performance of the entropy net with respect to the decision-tree classifier, it is observed that the entropy net is able to achieve its highest performance level in far fewer iterations than commonly required in the nonhierarchical feedforward networks trained using backpropagation. The reason for this lies in the coarse learning achieved during the binary tree development