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 …

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 …

One of the most challenging machine learning problems is a particular case of data
classification in which classes are hierarchically structured and objects can be assigned to …

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 …

In this survey we discuss the task of hierarchical classification. The literature about this field
is scattered across very different application domains and for that reason research in one …

Hierarchical multi-label text classification (HMTC) is a fundamental but challenging task of
numerous applications (eg, patent annotation), where documents are assigned to multiple …

Hierarchical multi-label classification (HMC) is a challenging classification task extending
standard multi-label classification problems by imposing a hierarchy constraint on the …

Hierarchical multi-label classification (HMC) is a variant of classification where instances
may belong to multiple classes at the same time and these classes are organized in a …

Recommending phrases from web pages for advertisers to bid on against search engine
queries is an important research problem with direct commercial impact. Most approaches …

Multi‐label learning is quite a recent supervised learning paradigm. Owing to its capabilities
to improve performance in problems where a pattern may have more than one associated …