Ensemble methods are considered the state‐of‐the art solution for many machine learning
challenges. Such methods improve the predictive performance of a single model by training …

Multilabel learning has become a relevant learning paradigm in the past years due to the
increasing number of fields where it can be applied and also to the emerging number of …

The increasing usage of machine-learning models in critical domains has recently stressed
the necessity of interpretable machine-learning models. In areas like healthcare, finary–the …

Several natural language processing (NLP) tasks are defined as a classification problem in
its most complex form: Multi-label Hierarchical Extreme classification, in which items may be …

Ensemble methods that train multiple learners and then combine them to use, with Boosting
and Bagging as representatives, are well-known machine learning approaches. It has …

Decision forests are considered the best practice in many machine learning challenges,
mainly due to their superior predictive performance. However, simple models like decision …

A decision tree is a predictive model that recursively partitions the covariate's space into
subspaces such that each subspace constitutes a basis for a different prediction function …

The idea of ensemble methodology is to build a predictive model by integrating multiple
models. It is well-known that ensemble methods can be used for improving prediction …

A simple yet effective multilabel learning method, called label powerset (LP), considers each
distinct combination of labels that exist in the training set as a different class value of a single …

A large body of research in supervised learning deals with the analysis of single-label data,
where training examples are associated with a single label λ from a set of disjoint labels L …