Multi-label classification is an approach to classification problems that allows each data point to be assigned to more than one class at the same time. Real life machine learning problems are often multi-label in nature—for example image labelling, topic identification in texts, and gene expression prediction. Many multi-label classification algorithms have been proposed in the literature and,...

We extend the multi-label classification setting with constraints on labels. This leads to two new machine learning tasks: First, the label constraints must be properly integrated into the classification process to improve its performance and second, we can try to automatically derive useful constraints from data. In this paper, we experiment with two constraint-based correction approaches as p...

This paper proposes an ensemble method for multilabel classification. The RAndom k-labELsets (RAKEL) algorithm constructs each member of the ensemble by considering a small random subset of labels and learning a single-label classifier for the prediction of each element in the powerset of this subset. In this way, the proposed algorithm aims to take into account label correlations using single-...

Background: According to Cobanoglu et al and Murphy, it is now widely

Multi-label classification deals with the problem where each instance is associated with multiple class labels. Because evaluation in multi-label classification is more complicated than singlelabel setting, a number of performance measures have been proposed. It is noticed that an algorithm usually performs differently on different measures. Therefore, it is important to understand which algori...

In multi-label classification tasks, labels are commonly related with each other. It has been well recognized that utilizing label relationship is essential to multi-label learning. One way to utilizing label relationship is to map labels to a lower-dimensional space of uncorrelated labels, where the relationship could be encoded in the mapping. Previous linear mapping methods commonly result i...

Many existing approaches employ one-vs-rest method to decompose a multi-label classification problem into a set of 2class classification problems, one for each class. This method is valid in traditional single-label classification, it, however, incurs training inconsistency in multi-label classification, because in the latter a data point could belong to more than one class. In order to deal wi...

In recent years, multi-label classification has attracted a significant body of research, motivated by real-life applications, such as text classification and medical diagnoses. Although sparsely studied in this context, Learning Classifier Systems are naturally well-suited to multi-label classification problems, whose search space typically involves multiple highly specific niches. This is the...