FHBF: Federated hybrid boosted forests with dropout rates for supervised learning tasks across highly imbalanced clinical datasets.
Vasileios C. PezoulasFanis G. KalatzisThemis P. ExarchosAndreas GoulesAthanasios G. TzioufasDimitrios I. FotiadisPublished in: Patterns (2024)
Keyphrases
- supervised learning tasks
- highly imbalanced
- supervised learning
- data sets
- random forests
- class distribution
- feature selection
- active learning
- semi supervised learning
- multiple kernel learning
- imbalanced data
- semi supervised
- sample selection
- unlabeled data
- benchmark datasets
- similarity measure
- pairwise
- data mining
- database