Boosting semi-supervised learning under imbalanced regression via pseudo-labeling.
Nannan ZongSongzhi SuChangle ZhouPublished in: Concurr. Comput. Pract. Exp. (2024)
Keyphrases
- semi supervised learning
- label propagation
- boosting framework
- active learning
- unsupervised learning
- semisupervised learning
- semi supervised
- unlabeled data
- labeled data
- semi supervised classification
- class imbalance
- machine learning
- partially labeled
- supervised learning
- co training
- model selection
- base learners
- binary classification problems
- training data
- learning problems
- manifold regularization
- text categorization
- learning algorithm
- improve the classification accuracy
- cost sensitive
- labeled and unlabeled data
- feature selection
- semi supervised learning algorithms
- graph based semi supervised learning
- ensemble methods
- ensemble learning
- image segmentation
- learning models
- class distribution
- support vector
- feature space
- labeled examples
- regression problems
- dimensionality reduction
- data mining
- decision trees
- reproducing kernel hilbert space
- domain adaptation
- transfer learning
- class labels
- loss function
- training examples
- object recognition
- training set
- principal component analysis