- Location
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Deutsche Nationalbibliothek Frankfurt am Main
- Extent
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1 Online-Ressource.
- Language
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Englisch
- Bibliographic citation
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Resampling strategies for imbalanced regression: a survey and empirical analysis ; volume:57 ; number:4 ; day:4 ; month:3 ; year:2024 ; pages:1-42 ; date:4.2024
Artificial intelligence review ; 57, Heft 4 (4.3.2024), 1-42, 4.2024
- Creator
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Avelino, Juscimara G.
Cavalcanti, George D. C.
Cruz, Rafael M. O.
- Contributor
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SpringerLink (Online service)
- DOI
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10.1007/s10462-024-10724-3
- URN
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urn:nbn:de:101:1-2406262111551.286659318963
- Rights
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
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14.08.2025, 10:56 AM CEST
Data provider
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Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Avelino, Juscimara G.
- Cavalcanti, George D. C.
- Cruz, Rafael M. O.
- SpringerLink (Online service)
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Correction to: WSMOTER: a novel approach for imbalanced regression
Microdata Disclosure by Resampling: Empirical Findings for Business Survey Data
Microdata Disclosure by Resampling - Empirical Findings for Business Survey Data
Risk factor analysis of device-related infections: value of re-sampling method on the real-world imbalanced dataset
Resampling imbalanced data for network intrusion detection datasets
An empirical study using permutation-based resampling in meta-regression
Re-sampling in instrumental variables regression
Density-based weighting for imbalanced regression
Imbalanced regression using regressor-classifier ensembles
RB-CCR: Radial-Based Combined Cleaning and Resampling algorithm for imbalanced data classification
WSMOTER: a novel approach for imbalanced regression
GMMSampling: a new model-based, data difficulty-driven resampling method for multi-class imbalanced data
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