Knowledge representation and inference in similarity networks and Bayesian multinets
Artificial Intelligence
Neural networks for pattern recognition
Neural networks for pattern recognition
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
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
Feature subset selection by Bayesian network-based optimization
Artificial Intelligence
Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
Feature Selection for Knowledge Discovery and Data Mining
Feature Selection for Knowledge Discovery and Data Mining
Machine Learning
A theoretical framework for possibilistic independence in a weakly ordered setting
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Learning Bayesian network classifiers by maximizing conditional likelihood
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Nearest neighbor pattern classification
IEEE Transactions on Information Theory
Uncertainty in clustering and classification
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
Arabic morphological analysis and disambiguation using a possibilistic classifier
ICIC'12 Proceedings of the 8th international conference on Intelligent Computing Theories and Applications
Fuzzy machine learning and data mininga
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
Naive possibilistic classifiers for imprecise or uncertain numerical data
Fuzzy Sets and Systems
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Naive Bayesian network classifiers have proved their effectiveness to accomplish the classification task, even if they work under the strong assumption of independence of attributes in the context of the class node. However, as all of them are based on probability theory, they run into problems when they are faced with imperfection. This paper proposes a new approach of classification under the possibilistic framework with naive classifiers. To output the naive possibilistic network classifier, two procedures are studied namely the building phase, which deals with imperfect (imprecise/uncertain) dataset attributes and classes, and the classification phase, which is used to classify new instances that may be characterized by imperfect attributes. To improve the performance of our classifier, we propose two extensions namely selective naive possibilistic classifier and semi-naive possibilistic classifier. Experimental study has shown naive Bayes style possibilistic classifier, and is efficient in the imperfect case.