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 …

Feature selection has been an important issue in machine learning and data mining, and is
unavoidable when confronting with high‐dimensional data. With the advent of multilabel …

The k-NN algorithm is one of the most renowned ML algorithms widely used in the area of
data classification research. With the emergence of big data, the performance and the …

Recently, geolocalisation of tweets has become important for a wide range of real-time
applications, including real-time event detection, topic detection or disaster and emergency …

A multi-label dataset consists of observations associated with one or more outcomes. The
traditional classification task generalizes to the prediction of several class labels …

Multi-label classification is the challenging task of predicting the presence and absence of
multiple targets, involving representation learning and label correlation modeling. We …

We propose to solve a label ranking problem as a structured output regression task. In this
view, we adopt a least square surrogate loss approach that solves a supervised learning …

Multi-label document classification is a typical challenge in many real-world applications.
Multi-label ranking is a common approach, while existing studies usually disregard the …

This book is based on my Ph. D. dissertation completed at Ghent University (Belgium) and
the University of Granada (Spain) in June 2018. It focuses on classification. The goal is to …

Obtaining an up-to-date high-resolution description of land cover is a challenging task due
to the high cost and labor-intensive process of human annotation through field studies. This …