Constructing membership functions using statistical data
Fuzzy Sets and Systems
On the concept of possibility-probability consistency
Fuzzy Sets and Systems
The logical view of conditioning and its application to possibility and evidence theories
International Journal of Approximate Reasoning
Semi-naive Bayesian classifier
EWSL-91 Proceedings of the European working session on learning on Machine learning
When upper probabilities are possibility measures
Fuzzy Sets and Systems - Special issue dedicated to Professor Claude Ponsard
On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
Machine Learning - Special issue on learning with probabilistic representations
Machine Learning - Special issue on learning with probabilistic representations
Learning in graphical models
An overview of ordinal and numerical approaches to causal diagnostic problem solving
Handbook of defeasible reasoning and uncertainty management systems
A theoretical framework for possibilistic independence in a weakly ordered setting
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
EMMCVPR '99 Proceedings of the Second International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition
Possibilistic instance-based learning
Artificial Intelligence - Special issue: Fuzzy set and possibility theory-based methods in artificial intelligence
Learning Bayesian network classifiers by maximizing conditional likelihood
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Decision trees as possibilistic classifiers
International Journal of Approximate Reasoning
Case-based learning in a bipolar possibilistic framework
International Journal of Intelligent Systems - Bipolar Representations of Information and Preference Part 2: Reasoning and Learning
An Efficient Algorithm for Naive Possibilistic Classifiers with Uncertain Inputs
SUM '08 Proceedings of the 2nd international conference on Scalable Uncertainty Management
Consonant Belief Function Induced by a Confidence Set of Pignistic Probabilities
ECSQARU '07 Proceedings of the 9th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Bayesian classifiers based on kernel density estimation: Flexible classifiers
International Journal of Approximate Reasoning
A Rule-Based Classification Algorithm for Uncertain Data
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Naïve possibilistic network classifiers
Fuzzy Sets and Systems
Possibility theory and statistical reasoning
Computational Statistics & Data Analysis
Fuzzy methods in machine learning and data mining: Status and prospects
Fuzzy Sets and Systems
A Bayesian classifier for uncertain data
Proceedings of the 2010 ACM Symposium on Applied Computing
An analysis of Bayesian classifiers
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
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
Comparing Bayesian network classifiers
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Estimating continuous distributions in Bayesian classifiers
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Induction of selective Bayesian classifiers
UAI'94 Proceedings of the Tenth international conference on Uncertainty in artificial intelligence
SUM'12 Proceedings of the 6th international conference on Scalable Uncertainty Management
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In real-world problems, input data may be pervaded with uncertainty. In this paper, we investigate the behavior of naive possibilistic classifiers, as a counterpart to naive Bayesian ones, for dealing with classification tasks in the presence of uncertainty. For this purpose, we extend possibilistic classifiers, which have been recently adapted to numerical data, in order to cope with uncertainty in data representation. Here the possibility distributions that are used are supposed to encode the family of Gaussian probabilistic distributions that are compatible with the considered dataset. We consider two types of uncertainty: (i) the uncertainty associated with the class in the training set, which is modeled by a possibility distribution over class labels, and (ii) the imprecision pervading attribute values in the testing set represented under the form of intervals for continuous data. Moreover, the approach takes into account the uncertainty about the estimation of the Gaussian distribution parameters due to the limited amount of data available. We first adapt the possibilistic classification model, previously proposed for the certain case, in order to accommodate the uncertainty about class labels. Then, we propose an algorithm based on the extension principle to deal with imprecise attribute values. The experiments reported show the interest of possibilistic classifiers for handling uncertainty in data. In particular, the probability-to-possibility transform-based classifier shows a robust behavior when dealing with imperfect data.