Enriching relational learning with fuzzy predicates

Abstract

The interest of introducing fuzzy predicates when learning rules is twofold, When dealing with numerical data, it enables us to avoid arbitrary discretization. Moreover, it enlarges the expressive power of what is learned by considering different types of fuzzy rules, which may describe gradual behaviors of related attributes or uncertainty pervading conclusions. This paper describes different types of first-order fuzzy rules and a method for learning each type. Finally, we discuss the interest of each type of rules on a benchmark example.