In this paper we present TANC, i.e., a tree-augmented naive credal classifier based on imprecise probabilities; it models prior near-ignorance via the Extreme Imprecise Dirichlet ...
In the last 10 years, there has been increasing interest in interval valued data in signal processing. According to the conventional view, an interval value supposedly reflects th...
In the original formulation of influence diagrams (IDs), each model contained exactly one utility node. Tatman and Shachter (1990), introduced the possibility of having super-valu...
We explore generalizations of the pari-mutuel model (PMM), a formalization of an intuitive way of assessing an upper probability from a precise one. We discuss a naive extension o...
The present article considers estimating a parameter θ in an imprecise probability model (Pθ)θ∈Θ which consists of coherent upper previsions Pθ . After the definition of a...
For diagnosing dyslexia in early childhood, children have to solve non-writing based, graphical tests. Curently, these tests are processed by a human expert; applying artificial ...
Following Breiman’s methodology, we propose a multi-classifier based on a “forest” of randomly generated fuzzy decision trees, i.e., a Fuzzy Random Forest. This approach co...
Bayesian networks with mixtures of truncated exponentials (MTEs) support efficient inference algorithms and provide a flexible way of modeling hybrid domains (domains containing ...
Helge Langseth, Thomas D. Nielsen, Rafael Rum&iacu...