Classifier Chains: A Review and Perspectives

TL;DR

This paper reviews the classifier chains method for multi-label learning, discussing its effectiveness, theoretical foundations, recent improvements, and future research directions, highlighting its continued relevance and potential in the field.

Contribution

It provides a comprehensive review and survey of classifier chains, summarizing recent advances, theoretical insights, and proposing future research perspectives in multi-label classification.

Findings

Classifier chains achieve state-of-the-art performance across datasets.

Recent improvements enhance training and inference procedures.

The method remains a leading approach for multi-label learning.

Abstract

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.