A survey on feature selection methods
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Initial feature pruning can improve out-of-sample model performance substantially.
“This kind of initial feature pruning can improve out-of-sample model performance substantially (80).”
From Using Administrative Data to Predict Suicide After Psychiatric Hospitalization in the Veterans Health Administration System (2020) · cites this paper as [80]
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“However, due to the high number of computations required to generate the feature subsets and evaluate them, wrapper methods are computationally heavy (relative to fi lter and embedded methods) (Chandrashekar and Sahin, 2014).”
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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)
- 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 · 2012: 1
- 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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