Nowadays, with emerge the multi-label datasets, the multi-label learning processes attracted
interest and increasingly applied to different fields. In such learning processes, unlike single …

Many of the existing machine learning algorithms, both supervised and unsupervised,
depend on the quality of the input characteristics to generate a good model. The amount of …

This book is concerned with the classification of multilabeled data and other tasks related to
that subject. The goal of this chapter is to formally introduce the problem, as well as to give a …

Abstract Machine learning is a field which studies how machines can alter and adapt their
behavior, improving their actions according to the information they are given. This field is …

Machine learning has become an everyday tool in so many fields that there is plenty of
software to run many of these algorithms in every device, from supercomputers to embedded …

Multilabel classification is an emergent data mining task with a broad range of real world
applications. Learning from imbalanced multilabel data is being deeply studied latterly, and …

The learning from imbalanced data is a deeply studied problem in standard classification
and, in recent times, also in multilabel classification. A handful of multilabel resampling …

Nowadays, cities are dynamic ecosystems where urban changes occur at a very fast pace.
Hence, social sensing has become a powerful tool to uncover the actual land-use of a …

Investigating strategies that are able to efficiently deal with multi-label classification tasks is
a current research topic in machine learning. Many methods have been proposed, making …

New proposals in the field of multi-label learning algorithms have been growing in number
steadily over the last few years. The experimentation associated with each of them always …