Gene selection for cancer classification using support vector machines
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The data structure is captured by this efficient sparse coding model, which also finds correlations between different input vectors.
“The data structure is captured by this efficient sparse coding model, which also finds correlations between different input vectors [92].”
From Comparative Analysis of Intrusion Detection Systems and Machine Learning Based Model Analysis through Decision Tree (2023) · cites this paper as [92]
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“53 In paper R, they use the forward feature selection algorithm, a method that fi nds the most effective features in the classi fi cation output of the model.”
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- Authors
- Guyon, I., Weston, J., Barnhill, S., & Vapnik, V.
- Affiliations
- Georgia Institute of Technology (US)
- AT&T (United States) (US)
- Corresponding author
- Guyon, Isabelle
- Vapnik, Vladimir
- ORCID
- Guyon, Isabelle
- Venue
- Machine Learning
- Published
- 2002-01
- Volume
- 46
- Issue
- 1-3
- Pages
- 389-422
- ISSN
- 0885-6125 (ISSN-L)
- 1573-0565
- OpenAlex
- W2143426320
- Cited by
- 9,954
- References
- 47
- Citations by year
- 2026: 553 · 2025: 676 · 2024: 564 · 2023: 586 · 2022: 576 · 2021: 695 · 2020: 607 · 2019: 571 · 2018: 488 · 2017: 478 · 2016: 408 · 2015: 444 · 2014: 400 · 2013: 400 · 2012: 359
- FWCI
- 38.69
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Machine Learning (publishedVersion)
- Funders
- Delft Research Centres
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Breast Cancer Treatment Studies
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face and Expression Recognition
- Keywords
- gene selection, cancer classification, DNA microarrays, gene expression data, recursive feature elimination, support vector machine, leukemia, colon cancer, gene redundancy, genetic diagnosis, drug discovery
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