Rule acquisition based on hyperbox representation and its applications

Abstract

This paper gives introduction to four knowledge acquisition systems that use hyperboxes to represent knowledge. Hyperbox representation is interesting because it can uniformly represent both knowledge acquired by human experts and knowledge automatically learned from training examples. Potential applications of these systems are described. Discussions on performance issues and on future research topics are given.