Ensembles for feature selection: A review and future trends

View/ Open
Use this link to cite
http://hdl.handle.net/2183/35335
Except where otherwise noted, this item's license is described as Atribución-NoComercial-SinDerivadas 4.0 Internacional
Collections
- Investigación (FIC) [1615]
Metadata
Show full item recordTitle
Ensembles for feature selection: A review and future trendsDate
2019Citation
Bolón-Canedo, V. and Alonso-Betanzos, A. (2019) ‘Ensembles for Feature Selection: A Review and Future Trends’, Information Fusion, 52, pp. 1–12. doi:10.1016/j.inffus.2018.11.008.
Abstract
[Abstract]: 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.
Keywords
Ensemble learning
Feature selection
Feature selection
Description
© 2019. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/. This version of the article: Bolón-Canedo, V. and Alonso-Betanzos, A. (2019) ‘Ensembles for Feature Selection: A Review and Future Trends’ has been accepted for publication in: Information Fusion, 52, pp. 1–12. The Version of Record is available online at https://doi.org/10.1016/j.inffus.2018.11.008.
Editor version
Rights
Atribución-NoComercial-SinDerivadas 4.0 Internacional
ISSN
1566-2535
1872-6305
1872-6305