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Evaluating product-based possibilistic networks learning algorithms. (English) Zbl 06507026
Destercke, Sébastien (ed.) et al., Symbolic and quantitative approaches to reasoning with uncertainty. 13th European conference, ECSQARU 2015, Compiègne, France, July 15–17, 2015. Proceedings. Cham: Springer (ISBN 978-3-319-20806-0/pbk; 978-3-319-20807-7/ebook). Lecture Notes in Computer Science 9161. Lecture Notes in Artificial Intelligence, 312-321 (2015).
Summary: This paper proposes a new evaluation strategy for product-based possibilistic networks learning algorithms. The proposed strategy is mainly based on sampling a possibilistic networks in order to construct an imprecise data set representative of their underlying joint distribution. Experimental results showing the efficiency of the proposed method in comparing existing possibilistic networks learning algorithms is also presented.
For the entire collection see [Zbl 1316.68008].
68T37 Reasoning under uncertainty in the context of artificial intelligence
Full Text: DOI
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