Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
When upper probabilities are possibility measures
Fuzzy Sets and Systems - Special issue dedicated to Professor Claude Ponsard
Neural networks for pattern recognition
Neural networks for pattern recognition
Machine Learning - Special issue on learning with probabilistic representations
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
Decision trees as possibilistic classifiers
International Journal of Approximate Reasoning
An Efficient Algorithm for Naive Possibilistic Classifiers with Uncertain Inputs
SUM '08 Proceedings of the 2nd international conference on Scalable Uncertainty Management
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
An analysis of Bayesian classifiers
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
Estimating continuous distributions in Bayesian classifiers
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Possibilistic classifiers for uncertain numerical data
ECSQARU'11 Proceedings of the 11th European conference on Symbolic and quantitative approaches to reasoning with uncertainty
A possibilistic query translation approach for cross-language information retrieval
ICIC'13 Proceedings of the 9th international conference on Intelligent Computing Theories and Technology
Naive possibilistic classifiers for imprecise or uncertain numerical data
Fuzzy Sets and Systems
| Hi-index | 0.00 |
Naïve Bayesian classifiers are well-known for their simplicity and efficiency. They rely on independence hypotheses, together with a normality assumption, which may be too demanding, when dealing with numerical data. Possibility distributions are more compatible with the representation of poor data. This paper investigates two kinds of possibilistic elicitation methods that will be embedded into possibilistic naïve classifiers. The first one is derived from a probability-possibility transformation of Gaussian distributions (or mixtures of them), which introduces some further tolerance. The second kind is based on a direct interpretation of data in fuzzy histogram or possibilistic formats that exploit an idea of proximity between attribute values in different ways. Besides, possibilistic classifiers may be allowed to leave the classification open between several classes in case of insufficient information for choosing one (which may be of interest when the number of classes is large). The experiments reported show the interest of possibilistic classifiers.