The DOI matches its Crossref record; 84 of the 92 references it cites were found in a registry or an index.
A survey on feature selection methods
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In the citing paper
“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]
Citations
Cites
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Genetic algorithms in search, optimization, and machine learning
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LIBSVM: A library for support vector machines
Chang, C.-C., & Lin, C.-J. (2011) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/1961189.1961199 · Cited by 40,918
An Introduction to the Bootstrap
Efron, B., & Tibshirani, R. J. (1994) · doi:10.1201/9780429246593 · Cited by 40,500
Neural Networks: A Comprehensive Foundation
Haykin, S. (1998) · Cited by 30,070
Genetic Algorithms in Search, Optimization and Machine Learning
Goldberg, D. E., & Robson, D. (1988) · Cited by 18,391
Goldberg, D. E., & Shakespeare, W. (2002) · Cited by 16,088
A training algorithm for optimal margin classifiers
Boser, B. E., Guyon, I. M., & Vapnik, V. N. (1992) · Proceedings of the fifth annual workshop on Computational learning theory · doi:10.1145/130385.130401 · Cited by 11,992
Peng, H., Long, F., & Ding, C. (2005) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2005.159 · Cited by 10,818
Gene selection for cancer classification using support vector machines
Guyon, I., Weston, J., Barnhill, S., & Vapnik, V. (2002) · Machine Learning · doi:10.1023/a:1012487302797 · Cited by 9,954
Research works printed but not linked: 5
- Koller D, Sahami M. Towards optimal feature selection. In: ICML, vol. 96; 1996. p. 284–92.
- Acuna, 2003, A comparison of feature selection procedures for classifier based on kernel density estimation, Proc Comput Commun Control Technol, 1, 468
- Stearns S. On selecting features for pattern classifiers. In: Proceedings of the 3rd international conference on pattern recognition; 1976. p. 71–5.
- Peng, 2009, Lazy learner text categorization algorithm based on embedded feature selection, J Syst Eng Electron, 20, 651
- Dietterich, 1997, Machine learning research: four current directions, Artif Intell Mag, 18, 97
Cited by
Sorted by citations
Feature Selection: A Data Perspective
Li, J., Cheng, K., Wang, S., Morstatter, F., Trevino, R. P., Tang, J., & Liu, H. (2018) · ACM Computing Surveys · doi:10.1145/3136625 · Cited by 2,442
Binary grey wolf optimization approaches for feature selection
Emary, E., Zawbaa, H. M., & Hassanien, A. E. (2015) · Neurocomputing · doi:10.1016/j.neucom.2015.06.083 · Cited by 1,448
Relief-based feature selection: Introduction and review
Urbanowicz, R. J., Meeker, M., La Cava, W., Olson, R. S., & Moore, J. H. (2018) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2018.07.014 · Cited by 1,311
Hybrid Whale Optimization Algorithm with simulated annealing for feature selection
Mafarja, M. M., & Mirjalili, S. (2017) · Neurocomputing · doi:10.1016/j.neucom.2017.04.053 · Cited by 1,150
Global soil pollution by toxic metals threatens agriculture and human health
Hou, D., Jia, X., Wang, L., McGrath, S. P., Zhu, Y., Hu, Q., Zhao, F. J., Bank, M. S., O’Connor, D., & Nriagu, J. O. (2025) · Science · doi:10.1126/science.adr5214 · Cited by 1,121
Metadata
- 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
- 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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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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