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Multiple layer feedforward neural networks are often viewed as black boxes as the knowledge stored in the connection weights of these networks is generally considered incomprehensible. This paper suggests a solution to this deficiency of neural networks by proposing a backtracking tree search procedure for converting the weights of a neuron into a symbolic representation and demonstrating its use for understanding and symbolic approximation of feedforward neural networks. Several examples are presented to illustrate the proposed symbolic mapping of neurons.

Decision tree classifiers represent a popular classification methodology that has been successfully used in many pattern recognition tasks. Noting many similarities between the tree classifiers and the multilayer perceptron classifiers, an artificial neural network based implementation of decision tree classifiers to enhance their performance is described here. Several issues related to decision tree classifiers are discussed and it is shown how these can be dealt with following the proposed implementation.