A systematic review to identify feature selection publications in multi-labeled data

Feature selection enables the identification of important features in data sets, contributing to an eventual increase in the quality of the knowledge extracted from them. A kind of data of growing interest is the multi-labeled one, which has more than one label for each data instance. However, there is a lack of reviews about publications of feature selection to support 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 of feature selection in multi-labeled data? The systematic review process carried out in this report enabled us to select 49 relevant publications and to find some gaps in the current literature, which can inspire future research in this subject.

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