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A survey on feature selection methods

Chandrashekar, Girish, Sahin, Ferat·2014·Computers & Electrical Engineering

DOI 10.1016/j.compeleceng.2013.11.024

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“As a crucial data preprocessing method, there exist some comprehensive surveys about FS [1], [2] and [13].”
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“ture selection is a widely researched topic [12, 24, 40] and the main approaches can be divided in three categories:”
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“Last but not least, some irrelevant noise (e.g., black background in images) and redundant noise (compared with the image of Rose Bukater, the image of Kate Winslet is highly similar and contains less useful information) impair the robustness and generalization of the model [4], [25].”
Background
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“For a more comprehensive understanding of the rationale behind the selection of each technique, we recommend to refer to the survey paper mentioned in [18], as the reported studies do not delve into extensive explanations.”
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Background
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“The authors presented a feature selection strategy to increase computing time, prediction performance, and data comprehension [6].”
Background
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“There are many feature selection techniques in the literature [113], and there is no rule of thumb for selecting the best one.”
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“There are three commonly used methods for feature selection: ﬁlters, wrappers, and embeddings [17].”
Method
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Background
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Authors: Chandrashekar, G., & Sahin, F.
Affiliations: Rochester Institute of Technology (US)
Corresponding author: Chandrashekar, Girish
ORCID: Sahin, Ferat
Venue: Computers & Electrical Engineering
Publisher: Elsevier BV
Published: 2014-01
Volume: 40
Issue: 1
Pages: 16-28
ISSN: 0045-7906 (ISSN-L)
DOI: 10.1016/j.compeleceng.2013.11.024
OpenAlex: W2167101736
Cited by: 5,189
References: 92
Citations by year: 2026: 351 · 2025: 517 · 2024: 575 · 2023: 652 · 2022: 655 · 2021: 651 · 2020: 498 · 2019: 440 · 2018: 378 · 2017: 239 · 2016: 138 · 2015: 73 · 2014: 21
FWCI: 68.25
Type: journal article
Language: en
Open access: closed
Locations: Computers & Electrical Engineering (publishedVersion)
Topics: Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Inference
Physical Sciences › Computer Science › Artificial Intelligence › Text and Document Classification Technologies
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face and Expression Recognition
Keywords: feature selection, high-dimensional data, filter methods, wrapper methods, embedded methods, dimensionality reduction, machine learning

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