Fuzzy Max-Min Classifiers Decide locally on the Basis of Two Attributes
Birka von Schmidt, Frank Klawonn · UPCommons institutional repository (Universitat Politècnica de Catalunya) · 1999
Fuzzy classification systems differ from fuzzy controllers in the form of their outputs. For classification problems a decision between a finite number of discrete classes has to be made, whereas in fuzzy control the output domain is usually continuous, i.e. a real interval. In this paper we consider fuzzy classification systems using the maxmin inference scheme and classifying an unknown datum on the basis of maximum matching, i.e. assigning it to the class appearing in the consequent of the rule whose premise fits best. We basically show that this inference scheme locally takes only two attributes (variables) into account for the classification decision. 1 Introduction From a theoretical point of view fuzzy controllers are a method to describe a real function R m ! R (or, in the case of multi-input, multi-output systems, R m ! R k ) assigning a real (control) value to a given tuple, point or vector of measured input values. There are a variety of different models of fuzzy 1 ...