On the induction of different kinds of first-order fuzzy rules

Abstract

The paper describes a method for inducing first-order rules with fuzzy predicates from a database. First, the paper makes a distinction between fuzzy rules allowing for some tolerance with respect to the interpretative scope of the predicates, and fuzzy rules aiming at expressing a set of ordinary rules in a global way. Moreover the paper only considers the induction of Horn-like implicative-based fuzzy rules. Specific confidence degrees are associated with each kind of fuzzy rules in the inductive process. This technique is illustrated on an experimental application. The interest of learning various types of fuzzy first-order logic expressions is emphasized.