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 …

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 …

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 …

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 …

Class imbalance is a common issue in the community of machine learning and data mining.
The class-imbalance distribution can make most classical classification algorithms neglect …

Label distribution covers a certain number of labels, representing the degree to which each
label describes the instance. When dealing with label ambiguity, label distribution could …

Multi-label learning (MLL) learns from the examples each associated with multiple labels
simultaneously, where the high cost of annotating all relevant labels for each training …

This paper gives an attempt to explore the manifold in the label space for multi-label
learning. Traditional label space is logical, where no manifold exists. In order to study the …

The modern battlefield is getting more complicated due to the increasing number of different
radiation sources as well as their fierce contention (interference) and confrontations …

Multi-label learning deals with cases where every instance corresponds to multiple labels.
The objective is to learn mapping from an instance to a relevant label set. Existing multi …