Extraction of knowledge from data using constrained neural networks

Abstract

This paper deals with two complementary problems: the problem of extracting knowledge from neural networks and the problem of inserting knowledge into neural networks. Our approach to the extraction of knowledge is essentially constraints-based. Local constraints are imposed on the neural network's weights and activities to make neural networkunits work as logical operators. We have modified two well-known learning algorithms, namely the simulated annealing and the backpropagation, with respect to imposed constraints. In the case of the non-empty domain theory, the knowledge insertion technique is used to impose global constraints to determine the neural network's topology and initialization according to a priori knowledge about the problem under study. The knowledge to be inserted can be expressed as a set of propositional rules. We report simulation results obtained by running our algorithms to extract boolean formulae.