A survey of evolutionary algorithms for supervised ensemble learning

This paper presents a comprehensive review of evolutionary algorithms that learn an ensemble of predictive models for supervised machine learning (classification and regression). We propose a detailed four-level taxonomy of studies in this area. The first level of the taxonomy categorizes studies ba...

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Veröffentlicht in:Knowledge engineering review Jg. 38
Hauptverfasser: Cagnini, Henry E. L., Das Dôres, Silvia C. N., Freitas, Alex A., Barros, Rodrigo C.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Cambridge, UK Cambridge University Press 01.01.2023
Schlagworte:
Ensemble learning
Evolutionary algorithms
Genetic algorithms
Machine learning
Neural networks
Prediction models
Review
Supervised learning
Taxonomy
ISSN:0269-8889, 1469-8005
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Zusammenfassung:This paper presents a comprehensive review of evolutionary algorithms that learn an ensemble of predictive models for supervised machine learning (classification and regression). We propose a detailed four-level taxonomy of studies in this area. The first level of the taxonomy categorizes studies based on which stage of the ensemble learning process is addressed by the evolutionary algorithm: the generation of base models, model selection, or the integration of outputs. The next three levels of the taxonomy further categorize studies based on methods used to address each stage. In addition, we categorize studies according to the main types of objectives optimized by the evolutionary algorithm, the type of base learner used and the type of evolutionary algorithm used. We also discuss controversial topics, like the pros and cons of the selection stage of ensemble learning, and the need for using a diversity measure for the ensemble’s members in the fitness function. Finally, as conclusions, we summarize our findings about patterns in the frequency of use of different methods and suggest several new research directions for evolutionary ensemble learning.
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ISSN:0269-8889
1469-8005
DOI:10.1017/S0269888923000024