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

Multi-label learning deals with problems where each example is represented by a single
instance while being associated with multiple class labels simultaneously. Binary relevance …

Concise and unambiguous assessment of a machine learning algorithm is key to classifier
design and performance improvement. In the multi-class classification task, where each …

This paper presents the input convex neural network architecture. These are scalar-valued
(potentially deep) neural networks with constraints on the network parameters such that the …

In recent years, a plethora of approaches have been proposed to deal with the increasingly
challenging task of multi‐output regression. This study provides a survey on state‐of‐the‐art …

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 …

In multi-label learning, objects are essentially related to multiple semantic meanings, and
the type of data is confronted with the impact of high feature dimensionality simultaneously …

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 …

This book is concerned with the classification of multilabeled data and other tasks related to
that subject. The goal of this chapter is to formally introduce the problem, as well as to give a …

Multi-label classification is a practical yet challenging task in machine learning related fields,
since it requires the prediction of more than one label category for each input instance. We …