Ensembles for feature selection: A review and future trends

•A review of ensembles for feature selection is described.•Current state-of-the-art is provided.•Types of ensembles, combination methods and evaluation measures are described.•Some challenges and future trends are provided. Ensemble learning is a prolific field in Machine Learning since it is based...

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Bibliographic Details
Published in:Information fusion 2019-12, Vol.52, p.1-12
Main Authors: Bolón-Canedo, Verónica, Alonso-Betanzos, Amparo
Format: Article
Language:English
Subjects:
Ensemble learning
Feature selection
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Summary:•A review of ensembles for feature selection is described.•Current state-of-the-art is provided.•Types of ensembles, combination methods and evaluation measures are described.•Some challenges and future trends are provided. Ensemble learning is a prolific field in Machine Learning since it is based on the assumption that combining the output of multiple models is better than using a single model, and it usually provides good results. Normally, it has been commonly employed for classification, but it can be used to improve other disciplines such as feature selection. Feature selection consists of selecting the relevant features for a problem and discard those irrelevant or redundant, with the main goal of improving classification accuracy. In this work, we provide the reader with the basic concepts necessary to build an ensemble for feature selection, as well as reviewing the up-to-date advances and commenting on the future trends that are still to be faced.
ISSN:1566-2535
1872-6305
DOI:10.1016/j.inffus.2018.11.008