Selective oversampling approach for strongly imbalanced data

Challenges posed by imbalanced data are encountered in many real-world applications. One of the possible approaches to improve the classifier performance on imbalanced data is oversampling. In this paper, we propose the new selective oversampling approach (SOA) that first isolates the most represent...

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Detalles Bibliográficos
Autores principales: Gnip, Peter, Vokorokos, Liberios, Drotár, Peter
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2021
Materias:
Data Mining and Machine Learning
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8237317/
https://www.ncbi.nlm.nih.gov/pubmed/34239981
http://dx.doi.org/10.7717/peerj-cs.604

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8237317/
https://www.ncbi.nlm.nih.gov/pubmed/34239981
http://dx.doi.org/10.7717/peerj-cs.604

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