Exabytes of data are generated daily by humans, leading to the growing needs for new
efforts in dealing with the grand challenges for multi-label learning brought by big data. For …

In recent years, it has been difficult for multilabel classification to obtain complete multilabel
data in real‐world applications, and even a large number of labels for training samples are …

Music listening context such as location or activity has been shown to greatly influence the
users' musical tastes. In this work, we study the relationship between user context and audio …

Motivation Side effects of drugs could cause severe health problems and the failure of drug
development. Drug–target interactions are the basis for side effect production and are …

Weakly-supervised multi-label learning has emerged as a hot topic more recently. Most
existing methods deal with such problem by learning from the data where the label …

The relationship between features and labels plays an important role in multi-label learning.
The purpose of multi-label learning is to learn a mapping from the feature space to the label …

Weakly supervised multi-label learning (WSML) concentrates on a more challenging multi-
label classification problem, where some labels in the training set are missing. Existing …

Partial multi-label learning (PML) aims to learn from the training data where each training
example is annotated with a candidate label set, among which only a subset is relevant …

Label distribution learning (LDL) is one of the paradigms for dealing with label ambiguity,
and it can learn the relative importance of each label to a particular instance. Most of the …

The problem of multi-label classification with missing labels (MLML) is a common challenge
that is prevalent in several domains, eg image annotation and auto-tagging. In multi-label …