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 …

A large body of research in supervised learning deals with the analysis of single-label data,
where training examples are associated with a single label λ from a set of disjoint labels L …

Multi-label learning originated from the investigation of text categorization problem, where
each document may belong to several predefined topics simultaneously. In multi-label …

In multilabel learning, each instance in the training set is associated with a set of labels and
the task is to output a label set whose size is unknown a priori for each unseen instance. In …

In multi-label learning, the training set is made up of instances each associated with a set of
labels, and the task is to predict the label sets of unseen instances. In this paper, this …

Common approaches to multi-label classification learn independent classifiers for each
category, and employ ranking or thresholding schemes for classification. Because they do …

A wide variety of software metrics have been proposed and a broad range of tools is
available to measure them. However, the effective use of software metrics is hindered by the …

Multi-label learning deals with the problem where each instance is associated with multiple
labels simultaneously. The task of this learning paradigm is to predict the label set for each …

Many classification problems require classifiers to assign each single document into more
than one category, which is called multi-labelled classification. The categories in such …

The problem of multilabel classification has attracted great interest in the last decade, where
each instance can be assigned with a set of multiple class labels simultaneously. It has a …