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Multilabel classification has become increasingly important for various use cases. Amongst the existing multilabel classification methods, problem transformation approaches, such as Binary Relevance, Pruned Problem Transformation, and Classifier Chains, are among the most popular, since they break a global multilabel classification problem into a set of smaller binary or multiclass classification problems, which are well understood and extensively researched. Transformation methods enable thedoi:10.1007/s10462-017-9556-4 fatcat:y2ho4l5ohjhk7h6frrmmtk6tlq