RB-CCR: Radial-Based Combined Cleaning and Resampling algorithm for imbalanced data classification

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Location: Deutsche Nationalbibliothek Frankfurt am Main

ISSN: 1573-0565

Extent: Online-Ressource

Language: Englisch

Notes: online resource.

Bibliographic citation: RB-CCR: Radial-Based Combined Cleaning and Resampling algorithm for imbalanced data classification ; volume:110 ; number:11-12 ; day:14 ; month:10 ; year:2021 ; pages:3059-3093 ; date:12.2021
Machine learning ; 110, Heft 11-12 (14.10.2021), 3059-3093, 12.2021

Creator: Koziarski, Michał
Bellinger, Colin
Woźniak, Michał

Contributor: SpringerLink (Online service)

DOI: 10.1007/s10994-021-06012-8

URN: urn:nbn:de:101:1-2022013021181712637005

Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.

Last update: 15.08.2025, 7:32 AM CEST

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Koziarski, Michał
Bellinger, Colin
Woźniak, Michał
SpringerLink (Online service)

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Resampling strategies for imbalanced regression: a survey and empirical analysis

GMMSampling: a new model-based, data difficulty-driven resampling method for multi-class imbalanced data

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Portfolio Resampling and Efficiency Issues

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Rampendaler Bauerschaft (RB 3, RB 4, RB 6, RB 15, RB 19, RB 36c, RB 40, RB 47, RB 50, RB 55, RB 60, RB 61, RB 62, RB 63, RB 64, RB 65, RB 81, RB 84, RB 85, RB 86, RB 87, RB 88RB 96, RB 100, RB 100, RB 101, RB 103, RB 105, RB 109, RB 113, RB 114, RB 115, RB 116, RB 117, RB 118

Resampling imbalanced data for network intrusion detection datasets

Resampling strategies for imbalanced regression: a survey and empirical analysis

GMMSampling: a new model-based, data difficulty-driven resampling method for multi-class imbalanced data

Iterative cleaning and learning of big highly-imbalanced fraud data using unsupervised learning

Risk factor analysis of device-related infections: value of re-sampling method on the real-world imbalanced dataset

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Resampling methods for dependent data

Portfolio Resampling and Efficiency Issues

Extremal index: estimation and resampling

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Archivale

Rampendaler Bauerschaft (RB 3, RB 4, RB 6, RB 15, RB 19, RB 36c, RB 40, RB 47, RB 50, RB 55, RB 60, RB 61, RB 62, RB 63, RB 64, RB 65, RB 81, RB 84, RB 85, RB 86, RB 87, RB 88RB 96, RB 100, RB 100, RB 101, RB 103, RB 105, RB 109, RB 113, RB 114, RB 115, RB 116, RB 117, RB 118

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RB-CCR: Radial-Based Combined Cleaning and Resampling algorithm for imbalanced data classification

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Other Objects (12)

Resampling imbalanced data for network intrusion detection datasets

Resampling strategies for imbalanced regression: a survey and empirical analysis

GMMSampling: a new model-based, data difficulty-driven resampling method for multi-class imbalanced data

Iterative cleaning and learning of big highly-imbalanced fraud data using unsupervised learning

Risk factor analysis of device-related infections: value of re-sampling method on the real-world imbalanced dataset

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Resampling methods for dependent data

Portfolio Resampling and Efficiency Issues

Extremal index: estimation and resampling

Improved Particle Filter Resampling Architectures

Re-sampling in instrumental variables regression

Rampendaler Bauerschaft (RB 3, RB 4, RB 6, RB 15, RB 19, RB 36c, RB 40, RB 47, RB 50, RB 55, RB 60, RB 61, RB 62, RB 63, RB 64, RB 65, RB 81, RB 84, RB 85, RB 86, RB 87, RB 88RB 96, RB 100, RB 100, RB 101, RB 103, RB 105, RB 109, RB 113, RB 114, RB 115, RB 116, RB 117, RB 118

Resampling imbalanced data for network intrusion detection datasets

Resampling strategies for imbalanced regression: a survey and empirical analysis

GMMSampling: a new model-based, data difficulty-driven resampling method for multi-class imbalanced data

Iterative cleaning and learning of big highly-imbalanced fraud data using unsupervised learning

Risk factor analysis of device-related infections: value of re-sampling method on the real-world imbalanced dataset

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Resampling methods for dependent data

Portfolio Resampling and Efficiency Issues

Extremal index: estimation and resampling

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Re-sampling in instrumental variables regression

Rampendaler Bauerschaft (RB 3, RB 4, RB 6, RB 15, RB 19, RB 36c, RB 40, RB 47, RB 50, RB 55, RB 60, RB 61, RB 62, RB 63, RB 64, RB 65, RB 81, RB 84, RB 85, RB 86, RB 87, RB 88RB 96, RB 100, RB 100, RB 101, RB 103, RB 105, RB 109, RB 113, RB 114, RB 115, RB 116, RB 117, RB 118

Resampling imbalanced data for network intrusion detection datasets

Resampling strategies for imbalanced regression: a survey and empirical analysis

GMMSampling: a new model-based, data difficulty-driven resampling method for multi-class imbalanced data

Iterative cleaning and learning of big highly-imbalanced fraud data using unsupervised learning

Risk factor analysis of device-related infections: value of re-sampling method on the real-world imbalanced dataset

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Portfolio Resampling and Efficiency Issues

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