Evaluation of the naive evidential classifier (NEC): a comparison between its two variants based on a real agronomic application

Authors:
Yosra Mazigh;Boutheina Ben Yaghlane;Sébastien Destercke
Affiliations:
LARODEC Laboratory, University of Tunis, Tunisia;LARODEC Laboratory, University of Carthage, Tunisia;CNRS UMR 7253 Heuristique et diagnostique des systèmes complexes, France
Venue:
SUM'12 Proceedings of the 6th international conference on Scalable Uncertainty Management
Year:
2012

Citing 2
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Inference in directed evidential networks based on the transferable belief model

International Journal of Approximate Reasoning
An introduction to the imprecise Dirichlet model for multinomial data

International Journal of Approximate Reasoning

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Abstract

We introduce the notion of naive evidential classifier. This classifier, which has a structure mirroring the naive Bayes classifier, is based on the Transferable Belief Model and uses mass assignments as its uncertainty model. This new method achieves more robust inferences, mainly by explicitly modeling imprecision when data are in little amount or are imprecise. After introducing the model and its inference process based on Smet's generalized Bayes theorem (GBT), we specify some possible methods to learn its parameters, based on the Imprecise Dirichlet Model (IDM) or on predictive belief functions. Some experimental results on an agronomic application are then given and evaluated.