Active Learning for Ordinal Classification Based on Adaptive Diversity-Based Uncertainty Sampling.
Deniu HePublished in: IEEE Access (2023)
Keyphrases
- uncertainty sampling
- active learning
- ordinal classification
- cost sensitive
- binary classification
- ensemble members
- experimental design
- ensemble methods
- random sampling
- query by committee
- misclassification costs
- generalization error
- multi class classification
- monotonicity constraints
- semi supervised
- learning algorithm
- machine learning
- training examples
- class imbalance
- supervised learning
- data sets
- training set
- class distribution
- labeled data
- multi class
- probability estimates
- base classifiers
- semi supervised learning
- unlabeled data
- naive bayes
- text categorization