This presents a range of theories about uncertainty, all of them mathematical and allowing
quantitative treatment. A definition of uncertainty is automatically associated with one of …

Previous studies about ensembles of classifiers for bankruptcy prediction and credit scoring
have been presented. In these studies, different ensemble schemes for complex classifiers …

In many areas of science and engineering, we have a class (“population”) of objects, and we
are interested in the values of one or several quantities characterizing objects from this …

In this study, hybrid integration of MultiBoosting based on two artificial intelligence methods
(the radial basis function network (RBFN) and credal decision tree (CDT) models) and …

Bayesian optimization (BO) with Gaussian processes (GPs) surrogate models is widely used
to optimize analytically unknown and expensive-to-evaluate functions. In this paper, we …

The representation and quantification of uncertainty has received increasing attention in
machine learning in the recent past. The formalism of credal sets provides an interesting …

Adequate uncertainty representation and quantification have become imperative in various
scientific disciplines, especially in machine learning and artificial intelligence. As an …

We present an application of the measure of total uncertainty on convex sets of probability
distributions, also called credal sets, to the construction of classification trees. In these …

Deng entropy has been proposed to measure the uncertainty degree of basic probability
assignment in evidence theory. In this paper, the condition of the maximum of Deng entropy …

Recently, an alternative measure of total uncertainty in Dempster–Shafer theory of evidence
(DST) has been proposed in place of the maximum entropy measure. It is based on the …