The paper introduces a novel method called Classifier Chains (CC) for multi-label classification, which is based on the binary relevance (BR) approach. BR transforms a multi-label problem into multiple binary problems, one for each label, but it does not directly model label correlations, which can lead to suboptimal performance. CC extends BR by incorporating label correlations into the training process, while maintaining low computational complexity. The authors demonstrate that CC can achieve high predictive performance and is scalable to large datasets. They also propose an ensemble framework, Ensembles of Classifier Chains (ECC), which further improves the performance and scalability of CC. Extensive empirical evaluations on various multi-label datasets using multiple evaluation metrics show that ECC outperforms or competes with state-of-the-art methods in terms of both predictive performance and time complexity. The paper discusses the advantages of CC over related methods, including its simplicity, efficiency, and ability to handle label correlations, and provides insights into the impact of different ensemble strategies on performance and scalability.The paper introduces a novel method called Classifier Chains (CC) for multi-label classification, which is based on the binary relevance (BR) approach. BR transforms a multi-label problem into multiple binary problems, one for each label, but it does not directly model label correlations, which can lead to suboptimal performance. CC extends BR by incorporating label correlations into the training process, while maintaining low computational complexity. The authors demonstrate that CC can achieve high predictive performance and is scalable to large datasets. They also propose an ensemble framework, Ensembles of Classifier Chains (ECC), which further improves the performance and scalability of CC. Extensive empirical evaluations on various multi-label datasets using multiple evaluation metrics show that ECC outperforms or competes with state-of-the-art methods in terms of both predictive performance and time complexity. The paper discusses the advantages of CC over related methods, including its simplicity, efficiency, and ability to handle label correlations, and provides insights into the impact of different ensemble strategies on performance and scalability.