Rules extraction from neural networks trained on multimedia data

Abstract

Since the universal approximation property of artificial neural networks was discovered in the late 1980s, i.e., their capability to arbitrarily well approximate nearly arbitrary relationships and dependences, a full exploitation of this property has been always hindered by the very low human-comprehensibility of the purely numerical representation that neural networks use for such relationships and dependences. The mainstream of attempts to mitigate that incomprehensibility are methods extracting, from the numerical representation, rules of some formal logic, which are in general viewed as human-comprehensible. Many dozens of such methods have already been proposed since the 1980s, differing in a number of diverse aspects. Due to that diversity, and also due to a close connection of the semantics of extracted rules to the repsective application domain, no rules extraction methods have ever become a standard, and it is always necessary to select a suitable method for the considered domain. Here, rules extraction from trained neural networks is employed for multimedia data, which is an increasingly important but also increasingly complex kind of data. Three particular rules extraction methods are considered and applied to the modalities recognized text data and the speech acoustic data, both of them with different subsets of features. A detailed comparison of the performance of the considered methods on those datasets is presented, and a statistical analysis of the obtained results is performed.