Incorporating label dependency into the binary relevance framework for multi-label classification

► Label dependency is an important intrinsic characteristic of multi-label learning. ► Some multi-label learning methods do not consider label dependency in their process. ► Multi-label learning methods must consider label dependency for better performance. ► This paper proposes a method, named BR+, which aims to discover label dependency. ► BR+ outperforms similar multi-label learning methods in several cases. In multi-label classification, examples can be associated with multiple labels simultaneously. The task of learning from multi-label data can be addressed by methods that transform the multi-label classification problem into several single-label classification problems. The binary relevance approach is one of these methods, where the multi-label learning task is decomposed into several independent binary classification problems, one for each label in the set of labels, and the final labels for each example are determined by aggregating the predictions from all binary classifiers. However, this approach fails to consider any dependency among the labels. Aiming to accurately predict label combinations, in this paper we propose a simple approach that enables the binary classifiers to discover existing label dependency by themselves. An experimental study using decision trees, a kernel method as well as Naïve Bayes as base-learning techniques shows the potential of the proposed approach to improve the multi-label classification performance.