The application of generalised fuzzy rules to machine learning and automated knowledge discovery

Notions of generalised fuzzy conditional and equivalence rules relative to a combination function are introduced and a framework for reasoning with such rules is described. The applicability of this framework to machine learning and knowledge discovery problems is demonstrated. Methods for the automatic generation of two particular types of generalised rule are proposed. The two rule forms considered are rules with weighted AND/OR combination functions, as suggested by Zimmerman and Zysno, and evidential logic equivalence rules as defined by Baldwin. The process of generating rule bases is divided into the problem of generating fuzzy sets from data and that of finding combination functions to optimise the performance of the system given these fuzzy sets. For the former problem a mass assignment based approach is adopted and for the latter semantic discrimination analysis is used in conjunction with customised optimisation algorithms. The potential of rule bases of both forms is illustrated by their application to a number of model and real world machine learning problems.