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A survey of genetic algorithms for multi-label classification

Goncalves, E.C., Freitas, Alex A., Plastino, Alexandre (2018) A survey of genetic algorithms for multi-label classification. In: Proceedings of the 2018 IEEE Congress on Evolutionary Computation (CEC 2018). Proceedings of the 2018 IEEE Congress on Evolutionary Computation (CEC 2018). . pp. 981-988. IEEE, New York, NY, USA ISBN 978-1-5090-6017-7. (doi:10.1109/CEC.2018.8477927) (KAR id:74161)

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In recent years, multi-label classification (MLC) has become an emerging research topic in big data analytics and machine learning. In this problem, each object of a dataset may belong to multiple class labels and the goal is to learn a classification model that can infer the correct labels of new, previously unseen, objects. This paper presents a survey of genetic algorithms (GAs) designed for MLC tasks. The study is organized in three parts. First, we propose a new taxonomy focused on GAs for MLC. In the second part, we provide an up-to-date overview of the work in this area, categorizing the approaches identified in the literature with respect to the taxonomy. In the third and last part, we discuss some new ideas for combining GAs with MLC.

Item Type: Conference or workshop item (Paper)
DOI/Identification number: 10.1109/CEC.2018.8477927
Uncontrolled keywords: machine learning, data mining, multi-label classification, evolutionary algorithms
Subjects: Q Science > Q Science (General) > Q335 Artificial intelligence
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Alex Freitas
Date Deposited: 29 May 2019 10:39 UTC
Last Modified: 08 Dec 2022 21:41 UTC
Resource URI: (The current URI for this page, for reference purposes)
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