Multi-label algorithms often use an identical feature space to build classification models for
all labels. However, labels generally express different semantic information and should have …

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 …

Like single-label learning, multi-label learning also suffers from the curse of dimensionality.
Due to the existence of high-dimensional data, feature selection as a preprocessing tool …

In multilabel learning, each training example is represented by a single instance, which is
relevant to multiple class labels simultaneously. Generally, all relevant labels are …

Understanding hydrogen adsorption on metal nanoparticles is a key prerequisite for
designing efficient electrocatalysts for water splitting and the hydrogen evolution reaction …

Multi-label feature selection plays an increasingly important role in alleviating the high
dimensionality of multi-label learning tasks. Most extant methods posit that the learning task …

In multi-view and multi-label learning, each example can be represented by multiple data
view features and annotated with a set of discrete non-exclusive labels. Missing label …

Predicting 30-day hospital readmission is a core research task in the development of
personalized healthcare. However, the imbalanced class distribution and the heterogeneity …

Multilabel classification (MLC) is a challenging task in real-world applications, such as
project document classification which led us to conduct this research. In the past decade …

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 …