Improving cervical cancer classification with imbalanced datasets combining taming transformers with T2T-ViT.
Chen ZhaoRenjun ShuaiLi MaWenjia LiuMenglin WuPublished in: Multim. Tools Appl. (2022)
Keyphrases
- imbalanced datasets
- cancer classification
- random forest
- gene selection
- feature selection
- microarray
- cost sensitive learning
- imbalanced data
- class imbalance
- gene expression profiles
- gene expression data
- microarray data
- decision trees
- class distribution
- feature selection algorithms
- ensemble methods
- sampling methods
- pattern recognition
- training dataset
- data analysis
- gene expression
- benchmark datasets
- minority class
- support vector machine svm
- image classification
- support vector machine