The notion of uncertainty is of major importance in machine learning and constitutes a key
element of machine learning methodology. In line with the statistical tradition, uncertainty …

In the last years, the application of artificial intelligence methods on credit risk assessment
has meant an improvement over classic methods. Small improvements in the systems about …

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

We propose a new definition of entropy of basic probability assignments (BPAs) in the
Dempster–Shafer (DS) theory of belief functions, which is interpreted as a measure of total …

In the area of classification, C4. 5 is a known algorithm widely used to design decision trees.
In this algorithm, a pruning process is carried out to solve the problem of the over-fitting. A …

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 …

The theory of Evidence, or Shafer-Dempster theory (DST), has been widely used in
applications. The DST is based on the concept of a basic probability assignment. An …

In this article we develop a method for conflict management within Dempster–Shafer theory.
The idea is that each piece of evidence is discounted in proportion to the degree that it …

Abstract The Random Forest classifier has been considered as an important reference in the
data mining area. The building procedure of its base classifier (a decision tree) is principally …

Uncertainty quantification and robustness to distribution shifts are important goals in
machine learning and artificial intelligence. Although Bayesian Neural Networks (BNNs) …