A theoretical framework for possibilistic independence in a weakly ordered setting
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Possibilistic instance-based learning
Artificial Intelligence - Special issue: Fuzzy set and possibility theory-based methods in artificial intelligence
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
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
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
From Bayesian classifiers to possibilistic classifiers for numerical data
SUM'10 Proceedings of the 4th international conference on Scalable uncertainty management
Naive possibilistic classifiers for imprecise or uncertain numerical data
Fuzzy Sets and Systems
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In many real-world problems, input data may be pervaded with uncertainty. Naive possibilistic classifiers have been proposed as a counterpart to Bayesian classifiers to deal with classification tasks in presence of uncertainty. Following this line here, we extend possibilistic classifiers, which have been recently adapted to numerical data, in order to cope with uncertainty in data representation. 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. 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 extension principle-based algorithm 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.