Evolutionary Mahalanobis Distance-Based Oversampling for Multi-Class Imbalanced Data Classification

The number of sensing data are often imbalanced across data classes, for which oversampling on the minority class is an effective remedy. In this paper, an effective oversampling method called evolutionary Mahalanobis distance oversampling (EMDO) is proposed for multi-class imbalanced data classific...

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Detalles Bibliográficos
Autores principales: Yao, Leehter, Lin, Tung-Bin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8512012/
https://www.ncbi.nlm.nih.gov/pubmed/34640936
http://dx.doi.org/10.3390/s21196616

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8512012/
https://www.ncbi.nlm.nih.gov/pubmed/34640936
http://dx.doi.org/10.3390/s21196616

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