Knowledge acquisition by random sets

Abstract

In this article we investigate knowledge acquisition (KA) and its relationships to random sets. Based on random set theory, we develop some estimation theorems and procedures for set-valued statistics such as nonparametric estimators. Under random interval assumption, we establish some special possibility distributions that can be easily implemented in KA tools. The knowledge studied here are rules describing relationships between various concepts, as used in diagnosis (pattern recognition) expert systems. © 1996 John Wiley & Sons, Inc.