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...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	Expert systems with applications Jg. 42; H. 9; S. 4239 - 4252
Hauptverfasser:	Kimovski, Dragi, Ortega, Julio, Ortiz, Andrés, Baños, Raúl
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier Ltd 01.06.2015
Schlagworte:	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
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!