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 …

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 …

The widely known binary relevance method for multi-label classification, which considers
each label as an independent binary problem, has often been overlooked in the literature …

Although field-collected recordings typically contain multiple simultaneously vocalizing birds
of different species, acoustic species classification in this setting has received little study so …

In recent years, the means of disease diagnosis and treatment have been improved
remarkably, along with the continuous development of technology and science …

Binary relevance (BR) learns a single binary model for each different label of multi-label
data. It has linear complexity with respect to the number of labels, but does not take into …

Gene function prediction is a complex computational problem, characterized by several
items: the number of functional classes is large, and a gene may belong to multiple classes; …

Multilabel classification is a challenging research problem in which each instance may
belong to more than one class. Recently, a considerable amount of research has been …

Multi-label classification is relevant to many domains, such as text, image and other media,
and bioinformatics. Researchers have already noticed that in multi-label data, correlations …

The purpose of this study was to develop and evaluate the video on demand learning
system. This study integrated the thematic instructional strategy into interactive video-based …