Logo do repositório
 
Publicação

imbalanced-learn-extra

2026-01-19Artigo científico Acesso aberto
Ver registo simples
dc.contributor.authorDouzas, Georgios
dc.contributor.authorBação, Fernando
dc.contributor.institutionInformation Management Research Center (MagIC) - NOVA Information Management School
dc.contributor.institutionNOVA Information Management School (NOVA IMS)
dc.contributor.pblUbiquity Press
dc.date.accessioned2026-01-23T10:43:01Z
dc.date.available2026-01-23T10:43:01Z
dc.date.issued2026-01-19
dc.descriptionDouzas, G., & Bação, F. (2026). imbalanced-learn-extra: A Python Package for Novel Oversampling Algorithms. Journal of Open Research Software, 14(1), Article 1. https://doi.org/10.5334/jors.459 --- This work was supported by national funds through FCT (Fundação para a Ciência e a Tecnologia), under the project – UID/04152/2025 – Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMS – https://doi.org/10.54499/UID/04152/2025 (2025-01-01/2028-12-31) and UID/PRR/04152/2025 https://doi.org/10.54499/UID/PRR/04152/2025 (2025-01-01/2026-06-30) and the project ENHANCE – Enhancing Sustainable Travel in Small Cities and Outer Metropolitan Areas (DUT/0004/2022) https://doi.org/10.54499/DUT/0004/2022.
dc.description.abstractLearning from imbalanced data is a common challenge in supervised learning, as most classifiers assume balanced class distributions. Among the strategies to mitigate this issue, oversampling algorithms offer a flexible and model-agnostic solution by generating synthetic samples for minority classes. In this paper, we introduce the imbalanced-learn-extra Python library, an open-source extension of the imbalanced-learn ecosystem that provides additional oversampling techniques for research and practical use. The library integrates seamlessly with Scikit-Learn, allowing users to easily incorporate it into existing workflows. It implements Geometric SMOTE, a geometrically enhanced drop-in replacement for the original SMOTE algorithm, and clustering-based oversampling methods such as KMeans-SMOTE and G-SOMO, which combine existing imbalanced-learn oversamplers with Scikit-Learn clustering algorithms to address within-class imbalances. Rather than re-assessing the performance of these algorithms, which has already been thoroughly evaluated in prior studies, this paper focuses on their software design, implementation, and practical use within a unified framework.en
dc.description.versionpublishersversion
dc.description.versionpublished
dc.format.extent10
dc.format.extent1633004
dc.identifier.doi10.5334/jors.459
dc.identifier.issn2049-9647
dc.identifier.otherPURE: 151021603
dc.identifier.otherPURE UUID: 0535d688-f3d1-4142-b065-205f6ca58da1
dc.identifier.otherScopus: 105030571433
dc.identifier.otherORCID: /0000-0002-0834-0275/work/203443186
dc.identifier.urihttp://hdl.handle.net/10362/199676
dc.identifier.urlhttps://www.scopus.com/pages/publications/105030571433
dc.identifier.urlhttps://doi.org/10.5281/zenodo.15561407
dc.identifier.urlhttps://github.com/georgedouzas/imbalanced-learn-extra
dc.language.isoeng
dc.peerreviewedyes
dc.relationhttps://doi.org/10.54499/UID/04152/2025
dc.relationhttps://doi.org/10.54499/UID/PRR/04152/2025
dc.relationhttps://doi.org/10.54499/DUT/0004/2022
dc.subjectMachine Learning Classification
dc.subjectImbalanced Learning
dc.subjectOversampling
dc.subjectClustering
dc.subjectGeometric SMOTE
dc.subjectScikit-Learn
dc.subjectSoftware
dc.subjectInformation Systems
dc.subjectLibrary and Information Sciences
dc.titleimbalanced-learn-extraen
dc.title.subtitleA Python Package for Novel Oversampling Algorithmsen
dc.typejournal article
degois.publication.issue1
degois.publication.titleJournal of Open Research Software
degois.publication.volume14
dspace.entity.typePublication
rcaap.rightsopenAccess

Ficheiros

Principais
A mostrar 1 - 1 de 1
Miniatura
Nome:
imbalanced-learn-extra.pdf
Tamanho:
1.56 MB
Formato:
Adobe Portable Document Format
Ver/Abrir

Coleções

NIMS: MagIC - Artigos em revista internacional com arbitragem científica (Peer-Review articles in international journals)