Multi-surrogate assisted multi-objective evolutionary algorithms for feature selection in regression and classification problems with time series data

Feature selection wrapper methods are powerful mechanisms for reducing the complexity of prediction models while preserving and even improving their precision. Meta-heuristic methods, such as multi-objective evolutionary algorithms, are commonly used as search strategies in feature selection wrapper...

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Veröffentlicht in:	Information sciences Jg. 622; S. 1064 - 1091
Hauptverfasser:	Espinosa, Raquel, Jiménez, Fernando, Palma, José
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier Inc 01.04.2023
Schlagworte:	Classification Feature selection Multi-objective evolutionary algorithms Regression Surrogate models Time series forecasting Regression Time series forecasting Feature selection Surrogate models Multi-objective evolutionary algorithms Classification
ISSN:	0020-0255, 1872-6291
Online-Zugang:	Volltext
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