Embedding Undersampling Rotation Forest for Imbalanced Problem

Rotation Forest is an ensemble learning approach achieving better performance comparing to Bagging and Boosting through building accurate and diverse classifiers using rotated feature space. However, like other conventional classifiers, Rotation Forest does not work well on the imbalanced data which...

Descripción completa

Detalles Bibliográficos
Autores principales: Guo, Huaping, Diao, Xiaoyu, Liu, Hongbing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2018
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6236578/
https://www.ncbi.nlm.nih.gov/pubmed/30515200
http://dx.doi.org/10.1155/2018/6798042

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6236578/
https://www.ncbi.nlm.nih.gov/pubmed/30515200
http://dx.doi.org/10.1155/2018/6798042

Ejemplares similares