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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Vydané v:	Information sciences Ročník 622; s. 1064 - 1091
Hlavní autori:	Espinosa, Raquel, Jiménez, Fernando, Palma, José
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier Inc 01.04.2023
Predmet:	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
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