Cargando…
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...
Autores principales: | Guo, Huaping, Diao, Xiaoyu, Liu, Hongbing |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2018
|
Materias: | |
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 |
Ejemplares similares
-
Overlap-Based Undersampling Method for Classification of Imbalanced Medical Datasets
por: Vuttipittayamongkol, Pattaramon, et al.
Publicado: (2020) -
Ensemble of Rotation Trees for Imbalanced Medical Datasets
por: Guo, Huaping, et al.
Publicado: (2018) -
Imbalanced Learning Based on Logistic Discrimination
por: Guo, Huaping, et al.
Publicado: (2016) -
The Use of Hellinger Distance Undersampling Model to Improve the Classification of Disease Class in Imbalanced Medical Datasets
por: Al-Shamaa, Zina Z. R., et al.
Publicado: (2020) -
Retracted: The Use of Hellinger Distance Undersampling Model to Improve the Classification of Disease Class in Imbalanced Medical Datasets
por: and Biomechanics, Applied Bionics
Publicado: (2023)