Parallel alternatives for evolutionary multi-objective optimization in unsupervised feature selection

•Multiobjective unsupervised feature selection with many decision variables is tackled.•EEG signals for Brain–Computer Interface (BCI) applications are used as benchmarks.•Cooperative evolutionary algorithms for multiobjective optimization are given.•Parallel implementations obtain quality results i...

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Vydané v:	Expert systems with applications Ročník 42; číslo 9; s. 4239 - 4252
Hlavní autori:	Kimovski, Dragi, Ortega, Julio, Ortiz, Andrés, Baños, Raúl
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier Ltd 01.06.2015
Predmet:	Computational efficiency Computing time Evolution Evolutionary Expert systems Feature selection High-dimensional data Human-computer interface Multi-objective clustering Optimization Parallel evolutionary algorithms Pattern recognition Speedup models Unsupervised classification High-dimensional data Speedup models Feature selection Multi-objective clustering Parallel evolutionary algorithms Unsupervised classification
ISSN:	0957-4174, 1873-6793
On-line prístup:	Získať plný text
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