Abstract Multi-Label Classification (MLC) is an extension of the standard single-label
classification where each data instance is associated with several labels simultaneously …

This paper considers the problem of multi-label thorax disease classification on chest X-ray
images. Identifying one or more pathologies from a chest X-ray image is often hindered by …

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 …

The k NN (k-nearest neighbors) classification algorithm is one of the most widely used non-
parametric classification methods, however it is limited due to memory consumption related …

In this paper, we provide the first systematic literature review of the intersection of two
research areas, Multi-Label Learning (MLL) and Software Engineering (SE). We refer to this …

Multi-label classification is a challenging issue in the data science community due to the
ambiguity of label semantics. Existing studies mainly focus on improving label association …

In traditional machine learning, classifiers training is typically undertaken in the setting of
single-task learning, so the trained classifier can discriminate between different classes …

In multi-label learning algorithms, the classification performance can be significantly
improved using global and local label correlation. However, the incompleteness of the label …

Abstract Multi-Label Stream Classification (MLSC) is the classification streaming examples
into multiple classes simultaneously. Since new classes may emerge during the streaming …

Online semi-supervised multi-label classification serves a practical yet challenging task
since only a small number of labeled instances are available in real streaming …