Ensembles for feature selection: A review and future trends

By Verónica Bolón-Canedo, Amparo Alonso-Betanzos. Information Fusion, 2019

468 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/2cad9a6260a0ecbade218695007dc8e984535d4a

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Citations in the corpus

Towards Understanding Ensemble, Knowledge Distillation and Self-Distillation in Deep Learning (2020, background): “On the theory side, there are quite a few works focusing on understanding the superior performance of ensemble from a principled perspective [12, 14, 29, 31–34, 37, 44, 47–49, 52, 72, 72, 75, 76].”
Metaheuristic Algorithms on Feature Selection: A Survey of One Decade of Research (2009-2019) (2021, methodology): “Literature review has been presented in different ﬁelds such as sentiment analysis [5], bioinformatics [6], ensemble learning [7] using metaheuristic algorithms.”
Machine learning and structural health monitoring overview with emerging technology and high-dimensional data source highlights (2021, background): “Additionally, these approaches can be combined to form an ensemble learning method built on the output of different algorithms or learners and is believed to produce a better selection process.(173) Though not many cases of ensemble feature selection are present in SHM, the proliferation and advance”
A Review of Feature Selection Methods for Machine Learning-Based Disease Risk Prediction (2022, methodology): “The particular strengths and weaknesses of each methodological category mean they are more suitable for particular use cases (Saeys et al., 2007; Okser et al., 2013; De et al., 2014; Remeseiro and Bolon-Canedo, 2019) ( Table 1 ).”
A comprehensive survey on recent metaheuristics for feature selection (2022, background): “prepared a survey about ensemble learning for feature selection [84] .”