Read, JessePfahringer, BernhardHolmes, GeoffreyFrank, Eibe2014-03-042014-03-0420092009Read, J., Pfahringer, B., Holmes, G., & Frank, E. (2009). Classifier chains for multi-label classification. In W. Buntine et al. (Eds.): ECML PKDD 2009, Part II, LNAI 5782 (pp. 254-269). Springer-Verlag Berlin Heidelberg.https://hdl.handle.net/10289/8541The widely known binary relevance method for multi-label classification, which considers each label as an independent binary problem, has been sidelined in the literature due to the perceived inadequacy of its label-independence assumption. Instead, most current methods invest considerable complexity to model interdependencies between labels. This paper shows that binary relevance-based methods have much to offer, especially in terms of scalability to large datasets. We exemplify this with a novel chaining method that can model label correlations while maintaining acceptable computational complexity. Empirical evaluation over a broad range of multi-label datasets with a variety of evaluation metrics demonstrates the competitiveness of our chaining method against related and state-of-the-art methods, both in terms of predictive performance and time complexity.encomputer sciencemulti-label classificationConference Contribution10.1007/978-3-642-04174-7_17