  The family of methods collectively known as classifier chains has become a
popular approach to multi-label learning problems. This approach involves
linking together off-the-shelf binary classifiers in a chain structure, such
that class label predictions become features for other classifiers. Such
methods have proved flexible and effective and have obtained state-of-the-art
empirical performance across many datasets and multi-label evaluation metrics.
This performance led to further studies of how exactly it works, and how it
could be improved, and in the recent decade numerous studies have explored
classifier chains mechanisms on a theoretical level, and many improvements have
been made to the training and inference procedures, such that this method
remains among the state-of-the-art options for multi-label learning. Given this
past and ongoing interest, which covers a broad range of applications and
research themes, the goal of this work is to provide a review of classifier
chains, a survey of the techniques and extensions provided in the literature,
as well as perspectives for this approach in the domain of multi-label
classification in the future. We conclude positively, with a number of
recommendations for researchers and practitioners, as well as outlining a
number of areas for future research.
