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 studies the problem where each example is represented by a single
instance while associated with a set of labels simultaneously. During the past decade …

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 …

In recent years, multi-label learning becomes a trending topic in machine learning and data
mining. This type of learning deals with data that each instance is associated with more than …

A simple yet effective multilabel learning method, called label powerset (LP), considers each
distinct combination of labels that exist in the training set as a different class value of a single …

The purpose of this paper is to analyze the imbalanced learning task in the multilabel
scenario, aiming to accomplish two different goals. The first one is to present specialized …

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 …

Learning from imbalanced data is a problem which arises in many real-world scenarios, so
does the need to build classifiers able to predict more than one class label simultaneously …

Multi‐label learning is quite a recent supervised learning paradigm. Owing to its capabilities
to improve performance in problems where a pattern may have more than one associated …

This paper aims to develop a combination method for the classification of power quality
complex disturbances based on ensemble empirical mode decomposition (EEMD) and …