A Symbolic Logic Approach of Deriving Initial Neural Network Configurations for Supervised Classification

Abstract

One of the problems encountered in neural network applications is the choice of a suitable initial neural network configuration for the given classification problem. We propose an idea of constructing initial neural network configurations by making use of decision trees and threshold logic. First, a decision tree is constructed from the given set of training patterns. Then the decision tree is translated into a neural network. Initial values for the weights and thresholds of the neural network are determined. Finally, the obtained neural network is trained by the back-propagation algorithm. Experimental results have shown that a neural network constructed in this manner learns fast and performs efficiently.