A systematic review on experimental multi-label learning.
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Multi-label learning deals with the classification problem where each example is associated with a set of labels, which are usually dependent. This research topic has emerged in recent years due to the increasing number of applications where examples are annotated with more than one label. However, there is a lack of reviews focusing on pieces of work which report experimental results for multi-label learning. To this end, the systematic review process can be useful to identify related publications in a wide, rigorous and replicable way. This work uses the systematic review process to answer the following research question: what are the publications which report experimental results for multi-label learning research? The systematic review process carried out in this work included the application of 16 selection criteria to narrow the literature review, as we are interested in papers which report specific classifier evaluation measures using datasets publicly available. Moreover, these datasets cannot be preprocessed. In the end, this process enabled us to select 64 relevant publications, as well as identify some interesting facts in the current literature.