On the Use of Min-Based Revision Under Uncertain Evidence for Possibilistic Classifiers

Possibilitic networks, which are compact representations of possibility distributions, are powerful tools for representing and reasoning with uncertain and incomplete knowledge. According to the operator conditioning is based on, there are two possibilistic settings: quantitative and qualitative. This paper deals with qualitative possibilistic network classifiers under uncertain inputs. More precisely, we first present and analyze Jeffrey’s rule for revising possibility distributions by uncertain observations in the qualitative setting. Then, we propose an efficient algorithm for revising possibility distributions encoded by naive possibilistic networks for classification purposes. This algorithm consists in a series of efficient and equivalent transformations of initial naive possibilistic classifiers. Keywords— Min-based possibilistic networks, classification under uncertain inputs