Relevance as a new measure of relative importance of sets of rules

Process modelling is an important discipline both in science and engineering. The complexity of many real world systems has lead to sophisticated modelling approaches, where both the accuracy and readability of the models are of great importance. Fuzzy modelling is such an approach, which uses well-established machine learning techniques, producing models with the capacity of integrating expert knowledge with real world observations. The behaviour of these models is described as a series of linguistic rules. The readability of the models is related to the number of rules used to describe the system. In order to define methodologies for organising the information describing a system, it is important to define metrics for the relative importance of a set of rules in the description of a given region of the input/output space. This paper addresses this problem, and a new concept is proposed: the relevance of a set of rules