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Classifier chains: A review and perspectives

Abstract
The family of methods collectively known as classifier chains has become a popular approach to multi-label learning problems. This approach involves chaining together off-the-shelf binary classifiers in a directed structure, such that individual 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 the underlying mechanism and efficacy, and investigation into how it could be improved. In the recent decade, numerous studies have explored the theoretical underpinnings of classifier chains, and many improvements have been made to the training and inference procedures, such that this method remains among the best 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 key issues for future research.
Type
Journal Article
Type of thesis
Series
Citation
Read, J, Pfahringer, B., Holmes, G., & Frank, E. (2021). Classifier chains: A review and perspectives. Journal of Artificial Intelligence Research, 70, 683–718. https://doi.org/10.1613/jair.1.12376
Date
2021
Publisher
AI Access Foundation
Degree
Supervisors
Rights
© 2021 AI Access Foundation. All rights reserved.