Classification with Uncertain Observations Using Possibilistic Networks

Abstract

In this paper, we address the problem of possibilistic network-based classification with uncertain inputs. Possibilistic networks are powerful tools for representing and reasoning with uncertain and incomplete information in the framework of possibility theory. We first consider the direct use of Jeffrey's rule in the framework of possibility theory in order to perform classification with uncertain inputs. Then we study the property of Markov-blanket in our context. Lastly, we propose an efficient algorithm for possibilistic classifiers with uncertain inputs ensuring the same classification results as using the possibilistic counterpart of Jeffrey's rule. Our algorithm performs this task in a polynomial time without assuming strong independence relations between observations.