A Boundary-Information-Based Oversampling Approach to Improve Learning Performance for Imbalanced Datasets

Oversampling is the most popular data preprocessing technique. It makes traditional classifiers available for learning from imbalanced data. Through an overall review of oversampling techniques (oversamplers), we find that some of them can be regarded as danger-information-based oversamplers (DIBOs)...

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Detalles Bibliográficos
Autores principales: Li, Der-Chiang, Shi, Qi-Shi, Lin, Yao-San, Lin, Liang-Sian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8947752/
https://www.ncbi.nlm.nih.gov/pubmed/35327833
http://dx.doi.org/10.3390/e24030322

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8947752/
https://www.ncbi.nlm.nih.gov/pubmed/35327833
http://dx.doi.org/10.3390/e24030322

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