Traditionally, machine learning algorithms have been evaluated in applications where assumptions can be reliably made about class priors and/or misclassification costs. In this pa...
Abstract-- In sensor environments and moving robot applications, the position of an object is often known imprecisely because of measurement error and/or movement of the object. In...
We focus on credal nets, which are graphical models that generalise Bayesian nets to imprecise probability. We replace the notion of strong independence commonly used in credal ne...
Gert de Cooman, Filip Hermans, Alessandro Antonucc...
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 ...
We consider the statistical problem of analyzing the association between two categorical variables from cross-classified data. The focus is put on measures which enable one to st...