Multi-label classification (MLC) studies the problem where each instance is associated with
multiple relevant labels, which leads to the exponential growth of output space. It confronts …

In multi-label classification tasks, labels are commonly related with each other. It has been
well recognized that utilizing label relationship is essential to multi-label learning. One way …

In multi-label learning, each object is represented by a single instance and is associated
with more than one class labels, where the labels might be correlated with each other. As we …

In multi-label learning, each object belongs to multiple class labels simultaneously. In the
data explosion age, the size of data is often huge, ie, large number of instances, features …

Learning label-specific features is an effective strategy for multi-label classification. Existing
multi-label classification methods for learning label-specific features face two challenges …

Multi-label classification has aroused extensive attention in various fields. With the
emergence of high-dimensional label space, academia has devoted to performing label …

Multi-label learning associates a given data instance with one or several class labels. A
frequent problem with real life multi-label datasets is the lack of complete label information …

Multi-label learning deals with the problem where each instance may be associated with
multiple labels simultaneously, and how to discover and exploit the label correlations is one …

To tackle a multi-label classification problem with many classes, recently label space
dimension reduction (LSDR) is proposed. It encodes the original label space to a low …

Multi-label learning is concerned with the classification of data with multiple class labels.
This is in contrast to the traditional classification problem where every data instance has a …