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Most supervised learning methods consider that each dataset instance is associated with a unique label. However, there are several domains in which the instances are associated with a set of labels (a multi-label). An alternative to investigate properties of multi-label data and their relationship with the learning performance consists in exploratory data analysis. This approach aims to obtain a better...
A controlled environment based on known properties of the dataset used by a learning algorithm is useful to empirically evaluate machine learning algorithms. Synthetic (artificial) datasets are used for this purpose. Although there are publicly available frameworks to generate synthetic single-label datasets, this is not the case for multi-label datasets, in which each instance is associated with...
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