A neighbourhood undersampling stacked ensemble with H-measure maximising meta-learner for imbalanced classification / Seng Zian
Stacked ensemble formulates an ensemble using a meta-learner to combine (stack) the predictions of multiple base classifiers. It suffers from the problem of suboptimal performance in imbalanced classification. Several underlying difficulty factors are reported to be responsible for performance de...
Saved in:
主要作者: | Seng , Zian |
---|---|
格式: | Thesis |
出版: |
2021
|
主题: | |
在线阅读: | http://studentsrepo.um.edu.my/14776/1/Seng_Zian.pdf http://studentsrepo.um.edu.my/14776/2/Seng_Zian.pdf http://studentsrepo.um.edu.my/14776/ |
标签: |
添加标签
没有标签, 成为第一个标记此记录!
|
相似书籍
-
A neighborhood undersampling stacked ensemble (NUS-SE) in imbalanced classification
由: Seng, Zian, et al.
出版: (2021) -
An empirical evaluation of stacked ensembles with different meta-learners in imbalanced classification
由: Zian, Seng, et al.
出版: (2021) -
Fuzzy distance-based undersampling technique for imbalanced flood data
由: Ku-Mahamud, Ku Ruhana, et al.
出版: (2016) -
Ensemble classifier and resampling for imbalanced multiclass learning
由: Sainin, Mohd Shamrie, et al.
出版: (2015) -
A conceptual model of enhanced undersampling technique
由: Zorkeflee, Maisarah, et al.
出版: (2014)