Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Graphical Models: Methods for Data Analysis and Mining
Graphical Models: Methods for Data Analysis and Mining
Learning from imprecise data: possibilistic graphical models
Computational Statistics & Data Analysis - Nonlinear methods and data mining
Possibilistic logic bases and possibilistic graphs
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
A possibility theory-oriented discussion of conceptual pattern structures
SUM'10 Proceedings of the 4th international conference on Scalable uncertainty management
From Bayesian classifiers to possibilistic classifiers for numerical data
SUM'10 Proceedings of the 4th international conference on Scalable uncertainty management
Possibilistic classifiers for uncertain numerical data
ECSQARU'11 Proceedings of the 11th European conference on Symbolic and quantitative approaches to reasoning with uncertainty
Possibilistic network-based classifiers: on the reject option and concept drift issues
SUM'11 Proceedings of the 5th international conference on Scalable uncertainty management
Inference in possibilistic network classifiers under uncertain observations
Annals of Mathematics and Artificial Intelligence
Naive possibilistic classifiers for imprecise or uncertain numerical data
Fuzzy Sets and Systems
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Possibilistic networks are graphical models particularly suitable for representing and reasoning with uncertain and incomplete information. According to the underlying interpretation of possibilistic scales, possibilistic networks are either quantitative or qualitative. In this paper, we address possibilistic-based classification with uncertain inputs. More precisely, we first analyze Jeffrey's rule for revising possibility distributions by uncertain observations. Then, we propose an efficient algorithm for revising possibility distributions encoded by a naive possibilistic network. This algorithm is particularly suitable for classification with uncertain inputs since it allows classification in polynomial time using different efficient transformations of initial naive possibilistic networks.