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 …

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 …

In the era of deep learning, loss functions determine the range of tasks available to models
and algorithms. To support the application of deep learning in multi-label classification …

This paper aims to develop a combination method for the classification of power quality
complex disturbances based on ensemble empirical mode decomposition (EEMD) and …

Chest X-ray (CXR) is one of the most common types of radiology examination for the
diagnosis of thorax diseases. Computer-aided diagnosis (CAD) was developed to help …

Multi-label classification studies the task where each example belongs to multiple labels
simultaneously. As a representative method, Ranking Support Vector Machine (Rank-SVM) …

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 …

A class of machine learning problem where each instance may either belong to one or more
than one class simultaneously is known as Multi-label classification problem. Unlike other …

Hybrid strategy, which generalizes a specific single-label algorithm while one or two data
decomposition tricks are applied implicitly or explicitly, has become an effective and efficient …