Multi-label learning studies the problem where each example is represented by a single
instance while associated with a set of labels simultaneously. During the past decade …

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 objective in extreme multi-label learning is to train a classifier that can automatically tag
a novel data point with the most relevant subset of labels from an extremely large label set …

It is well-known that exploiting label correlations is important to multi-label learning. Existing
approaches either assume that the label correlations are global and shared by all instances; …

This paper develops the Parabel algorithm for extreme multi-label learning where the
objective is to learn classifiers that can annotate each data point with the most relevant …

The objective in extreme multi-label classification is to learn a classifier that can
automatically tag a data point with the most relevant subset of labels from a large label set …

Multi-label learning has received significant attention in the research community over the
past few years: this has resulted in the development of a variety of multi-label learning …

Multi-label classification (MLC) has recently attracted increasing interest in the machine
learning community. Several studies provide surveys of methods and datasets for MLC, and …

scikit-multilearn is a Python library for performing multi-label classification. The library is
compatible with the scikit/scipy ecosystem and uses sparse matrices for all internal …

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 …