- Location
-
Deutsche Nationalbibliothek Frankfurt am Main
- ISSN
-
2196-1115
- Extent
-
Online-Ressource
- Language
-
Englisch
- Notes
-
online resource.
- Bibliographic citation
-
Feature selection methods and genomic big data: a systematic review ; volume:6 ; number:1 ; day:27 ; month:8 ; year:2019 ; pages:1-24 ; date:12.2019
Journal of Big Data ; 6, Heft 1 (27.8.2019), 1-24, 12.2019
- Creator
-
Tadist, Khawla
Najah, Said
Nikolov, Nikola S.
Mrabti, Fatiha
Zahi, Azeddine
- Contributor
-
SpringerLink (Online service)
- DOI
-
10.1186/s40537-019-0241-0
- URN
-
urn:nbn:de:101:1-2020010918594538986348
- Rights
-
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
-
14.08.2025, 10:56 AM CEST
Data provider
This object is provided by:
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Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Tadist, Khawla
- Najah, Said
- Nikolov, Nikola S.
- Mrabti, Fatiha
- Zahi, Azeddine
- SpringerLink (Online service)
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Analysis and Evaluation of Feature Selection and Feature Extraction Methods
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Accuracy of multi-trait genomic selection using different methods
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Correction to: Cooperative co‑evolution for feature selection in Big Data with random feature grouping
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Oral cancer prognosis based on clinicopathologic and genomic markers using a hybrid of feature selection and machine learning methods
Feature selection environment for genomic applications
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Building predictive models for feature selection in genomic mining
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Analysis and Evaluation of Feature Selection and Feature Extraction Methods
Cooperative co-evolution for feature selection in Big Data with random feature grouping
Accuracy of multi-trait genomic selection using different methods
A greedy feature selection algorithm for Big Data of high dimensionality
An adaptive threshold determination method of feature screening for genomic selection
Correction to: Cooperative co‑evolution for feature selection in Big Data with random feature grouping
Exploring the potential of incremental feature selection to improve genomic prediction accuracy