WSMOTER: a novel approach for imbalanced regression

to related objects

Location: Deutsche Nationalbibliothek Frankfurt am Main

Extent: 1 Online-Ressource.

Language: Englisch

Bibliographic citation: WSMOTER: a novel approach for imbalanced regression ; volume:54 ; number:19 ; day:26 ; month:6 ; year:2024 ; pages:8789-8799 ; date:10.2024
Applied intelligence ; 54, Heft 19 (26.6.2024), 8789-8799, 10.2024

Creator: Camacho, Luís
Bacao, Fernando

Contributor: SpringerLink (Online service)

DOI: 10.1007/s10489-024-05608-6

URN: urn:nbn:de:101:1-2411120851433.282729815757

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

Last update: 15.08.2025, 7:33 AM CEST

Data provider

This object is provided by:
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.

Show original at data provider

Associated

Camacho, Luís
Bacao, Fernando
SpringerLink (Online service)

Other Objects (12)

Correction to: WSMOTER: a novel approach for imbalanced regression

Density-based weighting for imbalanced regression

Imbalanced regression using regressor-classifier ensembles

Resampling strategies for imbalanced regression: a survey and empirical analysis

An automated approach for binary classification on imbalanced data

LoRAS: an oversampling approach for imbalanced datasets

A novel oversampling method based on Wasserstein CGAN for imbalanced classification

Optimizing neural networks for imbalanced data

A-SMOTE: A New Preprocessing Approach for Highly Imbalanced Datasets by Improving SMOTE

A systematic approach for learning imbalanced data: enhancing zero-inflated models through boosting

Bootstrapping binary GEV regressions for imbalanced datasets

Classification performance assessment for imbalanced multiclass data

Correction to: WSMOTER: a novel approach for imbalanced regression

Density-based weighting for imbalanced regression

Imbalanced regression using regressor-classifier ensembles

Resampling strategies for imbalanced regression: a survey and empirical analysis

An automated approach for binary classification on imbalanced data

LoRAS: an oversampling approach for imbalanced datasets

A novel oversampling method based on Wasserstein CGAN for imbalanced classification

Optimizing neural networks for imbalanced data

A-SMOTE: A New Preprocessing Approach for Highly Imbalanced Datasets by Improving SMOTE

A systematic approach for learning imbalanced data: enhancing zero-inflated models through boosting

Bootstrapping binary GEV regressions for imbalanced datasets

Classification performance assessment for imbalanced multiclass data

Correction to: WSMOTER: a novel approach for imbalanced regression

Density-based weighting for imbalanced regression

Imbalanced regression using regressor-classifier ensembles

Resampling strategies for imbalanced regression: a survey and empirical analysis

An automated approach for binary classification on imbalanced data

LoRAS: an oversampling approach for imbalanced datasets

A novel oversampling method based on Wasserstein CGAN for imbalanced classification

Optimizing neural networks for imbalanced data

A-SMOTE: A New Preprocessing Approach for Highly Imbalanced Datasets by Improving SMOTE

A systematic approach for learning imbalanced data: enhancing zero-inflated models through boosting

Bootstrapping binary GEV regressions for imbalanced datasets

Classification performance assessment for imbalanced multiclass data

Cultural heritage institutions wishing to register will find more information here.

Fields marked * need to be filled in.

Username*

Please enter your username

Email*

Please enter your email address

Please do not fill this field

First name

Last name

Password*

Please enter your password

Confirm password*

Please enter the same password

I have read the terms of use and the privacy policy for the collection of personal data and accept them. *

This field is required.

I would like to subscribe to the newsletter of the Deutsche Digitale Bibliothek. See newsletter subscription info.

Account created

Your "My DDB" account has been successfully created. Before you can log in to your account, you must click the confirmation link in the message we just sent to the email address you provided.

WSMOTER: a novel approach for imbalanced regression

Object Details

References and Relationships

Contributors, Places and Time

Further information

Data provider

Associated

Other Objects (12)

Correction to: WSMOTER: a novel approach for imbalanced regression

Density-based weighting for imbalanced regression

Imbalanced regression using regressor-classifier ensembles

Resampling strategies for imbalanced regression: a survey and empirical analysis

An automated approach for binary classification on imbalanced data

LoRAS: an oversampling approach for imbalanced datasets

A novel oversampling method based on Wasserstein CGAN for imbalanced classification

Optimizing neural networks for imbalanced data

A-SMOTE: A New Preprocessing Approach for Highly Imbalanced Datasets by Improving SMOTE

A systematic approach for learning imbalanced data: enhancing zero-inflated models through boosting

Bootstrapping binary GEV regressions for imbalanced datasets

Classification performance assessment for imbalanced multiclass data

Correction to: WSMOTER: a novel approach for imbalanced regression

Density-based weighting for imbalanced regression

Imbalanced regression using regressor-classifier ensembles

Resampling strategies for imbalanced regression: a survey and empirical analysis

An automated approach for binary classification on imbalanced data

LoRAS: an oversampling approach for imbalanced datasets

A novel oversampling method based on Wasserstein CGAN for imbalanced classification

Optimizing neural networks for imbalanced data

A-SMOTE: A New Preprocessing Approach for Highly Imbalanced Datasets by Improving SMOTE

A systematic approach for learning imbalanced data: enhancing zero-inflated models through boosting

Bootstrapping binary GEV regressions for imbalanced datasets

Classification performance assessment for imbalanced multiclass data

Correction to: WSMOTER: a novel approach for imbalanced regression

Density-based weighting for imbalanced regression

Imbalanced regression using regressor-classifier ensembles

Resampling strategies for imbalanced regression: a survey and empirical analysis

An automated approach for binary classification on imbalanced data

LoRAS: an oversampling approach for imbalanced datasets

A novel oversampling method based on Wasserstein CGAN for imbalanced classification

Optimizing neural networks for imbalanced data

A-SMOTE: A New Preprocessing Approach for Highly Imbalanced Datasets by Improving SMOTE

A systematic approach for learning imbalanced data: enhancing zero-inflated models through boosting

Bootstrapping binary GEV regressions for imbalanced datasets

Classification performance assessment for imbalanced multiclass data

Related objects

Reset password