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 …

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 …

The problem of network intrusion detection poses innumerable challenges to the research
community, industry, and commercial sectors. Moreover, the persistent attacks occurring on …

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 …

Demand-side management technology is a key element of the proposed smart grid, which
will help utilities make more efficient use of their generation assets by reducing consumers' …

Multi-label classification methods are increasingly required by modern applications, such as
protein function classification, music categorization, and semantic scene classification. This …

Stratified sampling is a sampling method that takes into account the existence of disjoint
groups within a population and produces samples where the proportion of these groups is …

During drug development, safety is always the most important issue, including a variety of
toxicities and adverse drug effects, which should be evaluated in preclinical and clinical trial …

This paper proposes an ensemble method for multilabel classification. The RAndom k-
labELsets (RAKEL) algorithm constructs each member of the ensemble by considering a …

Multi-label Learning is a form of supervised learning where the classification algorithm is
required to learn from a set of instances, each instance can belong to multiple classes and …