Partial, or set-valued classification assigns instances to sets of classes, making it possible to
reduce the probability of misclassification while still providing useful information. This paper …

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 applications of domain adaptation, there may exist multiple source domains, which can
provide more or less complementary knowledge for pattern classification in the target …

Credal networks generalize Bayesian networks to allow for imprecision in probability values.
This paper reviews the main results on credal networks under strong independence, as …

In machine learning, some models can make uncertain and imprecise predictions, they are
called evidential models. These models may also be able to handle imperfect labeling and …

In classification problem, several different classes may be partially overlapped in their
borders. The objects in the border are usually quite difficult to classify. A hybrid classification …

We propose a framework for descriptively analyzing sets of partial orders based on the
concept of depth functions. Despite intensive studies of depth functions in linear and metric …

Random forest is an efficient and accurate classification model, which makes decisions by
aggregating a set of trees, either by voting or by averaging class posterior probability …

With the increase in waste streams, industrial sorting has become a major issue. The main
challenge is to minimise sorting errors to avoid serious recycling problems and significant …

In cases of uncertainty, a multi-class classifier preferably returns a set of candidate classes
instead of predicting a single class label with little guarantee. More precisely, the classifier …