Abstract
Multi-label classification has gained in importance in the last decade and it is today confronted to the current needs to process massive raw data from heterogeneous sources. Therefore, dimensionality reduction, which aims at reducing the number of features, labels, or both, knows a renewed interest to enhance the scaling properties of the classifiers and their predictive performances. In this paper we review more than fifty papers presenting dimensionality reduction approaches for multi-label classification and we propose an analysis in three steps : (i) a typology of the methods describing the main components of their strategies, the problem they tackle and the way they solve it (ii) a unified formalization of the problems to help to distinguish the similarities and differences between the approaches, and (iii) a meta-analysis of the published experimental results inspired by the consensus theory to identify the most efficient algorithms.
Highlights
T HE most popular classification paradigms are the single label classification and the multi-class classification
In a Video on Demand catalog, a movie is described by a set of complementary labels (e.g. Funny, Masterpiece, Based on novel, Futuristic) which are used by a recommender system to provide users with movies that are relevant to their preferences
To tackle the complexity of the problems, a large number of multi-label dimensionality reduction methods have been published in the last decades
Summary
T HE most popular classification paradigms are the single label classification and the multi-class classification. The objective is to decide, for each instance described by its features, whether it is associated to a given label or not. Multi-label classification, which associates each instance to multiple labels, has received a great attention in recent years. [13], several reviews have been published [13] [14] [15] [16] [17] [18] They group the algorithms in three main families : (i) the problem transformation methods which transform the multi-label problem into one or several singlelabel classification or regression problems, (ii) the algorithm adaptation methods which adapt existing algorithms to learn from multi-label data and (iii) the ensemble methods which deduce multi-label predictions from a collection of learners
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