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 …

Background With the rapid accumulation of biological datasets, machine learning methods
designed to automate data analysis are urgently needed. In recent years, so-called topic …

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 deals with the problem where each example is represented by a single
instance (feature vector) while associated with a set of class labels. Existing approaches …

Neural networks have recently been proposed for multi-label classification because they are
able to capture and model label dependencies in the output layer. In this work, we …

Large multi-label datasets contain labels that occur thousands of times (frequent group),
those that occur only a few times (few-shot group), and labels that never appear in the …

Complex networks have been widely adopted to represent a variety of complicated systems.
Given a complex network, it is of great significance to perform accurate clustering for better …

Label distribution is more general than both single-label annotation and multi-label
annotation. It covers a certain number of labels, representing the degree to which each label …

Supervised text classification methods are efficient when they can learn with reasonably
sized labeled sets. On the other hand, when only a small set of labeled documents is …

Social media platforms such as (Twitter, Facebook, and Weibo) are being increasingly
embraced by individuals, groups, and organizations as a valuable source of information …