Meta-heuristic multi- and many-objective optimization techniques for solution of machine learning problems
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Data
2017-12-01
Autores
Rodrigues, Douglas
Papa, Joao P. [UNESP]
Adeli, Hojjat
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Editor
Wiley-Blackwell
Resumo
Recently, multi- and many-objective meta-heuristic algorithms have received considerable attention due to their capability to solve optimization problems that require more than one fitness function. This paper presents a comprehensive study of these techniques applied in the context of machine learning problems. Three different topics are reviewed in this work: (a) feature extraction and selection, (b) hyper-parameter optimization and model selection in the context of supervised learning, and (c) clustering or unsupervised learning. The survey also highlights future research towards related areas.
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machine learning, meta-heuristic algorithms, multi-objective optimization
Como citar
Expert Systems. Hoboken: Wiley, v. 34, n. 6, 12 p., 2017.