Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning
Learning in the presence of concept drift and hidden contexts
Machine Learning
The Optimum Class-Selective Rejection Rule
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning - Special issue on learning with probabilistic representations
Graphical Models: Methods for Data Analysis and Mining
Graphical Models: Methods for Data Analysis and Mining
On Unifying Probabilistic/Fuzzy and Possibilistic Rejection-Based Classifiers
SSPR '98/SPR '98 Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition
Flexible Control of Case-Based Prediction in the Framework of Possibility Theory
EWCBR '00 Proceedings of the 5th European Workshop on Advances in Case-Based Reasoning
A Two-Steps Algorithm for Min-Based Possibilistic Causal Networks
ECSQARU '01 Proceedings of the 6th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
An Efficient Algorithm for Naive Possibilistic Classifiers with Uncertain Inputs
SUM '08 Proceedings of the 2nd international conference on Scalable Uncertainty Management
Naïve possibilistic network classifiers
Fuzzy Sets and Systems
An Optimum Class-Rejective Decision Rule and Its Evaluation
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
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In this paper, we deal with two important issues regarding possibilistic network-based classifiers. The first issue addresses the reject option in possibilistic network-based classifiers. We first focus on simple threshold-based reject rules and provide interpretations for the ambiguity and distance reject then introduce a third reject kind named incompleteness reject occurring when the inputs are missing or incomplete. The second important issue we address is the one of concept drift. More specifically, we propose an efficient solution for revising a possibilistic network classifier with new information.