Journal
INFORMATION FUSION
Volume 52, Issue -, Pages 1-12Publisher
ELSEVIER
DOI: 10.1016/j.inffus.2018.11.008
Keywords
Ensemble learning; Feature selection
Funding
- Spanish Ministerio de Economa y Competitividad [TIN 2015-65069-C2-1-R]
- Xunta de Galicia [GRC2014/035, ED431G/01]
- European Union (FEDER/ERDF)
Ask authors/readers for more resources
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available