Multi-dimensional classification is a cutting-edge problem, in which the values of multiple
class variables have to be simultaneously assigned to a given example. It is an extension of …

Multidimensional Bayesian network classifiers have gained popularity over the last few
years due to their expressive power and their intuitive graphical representation. A drawback …

Multi-dimensional Bayesian network classifiers are Bayesian networks of restricted
topological structure, which are tailored to classifying data instances into multiple …

We present a general framework for multidimensional classification that captures the
pairwise interactions between class variables. The pairwise class interactions are encoded …

This work proposes an extended version of the well-known tree-augmented naive Bayes
(TAN) classifier where the structure learning step is performed without requiring features to …

A credal classifier for multilabel data is presented. This is obtained as an extension of
Zaffalon's naive credal classifier to the case of non-exclusive class labels. The dependence …

In this work, we tackle the problem of Multi-Label Classification (MLC) by using Cutset
Networks (CNets), weighted probabilistic model trees, recently proposed as\emphtractable …

Multi-Label classification consists of mapping each object to a set of relevant labels. A
successful approach to constructing Multi-Label classifiers is to obtain a probabilistic model …

This work proposes an extended version of the well-known tree-augmented naive Bayes
(TAN) classifier where the structure learning step is performed without requiring features to …

A detailed and extensive empirical study of empty prediction of multi-label classification is
conducted in this paper and to the best of our knowledge this work is the first empirical study …