An adaptive multi-class imbalanced classification framework based on ensemble methods and deep network.
Xuezheng JiangJunyi WangQinggang MengMohamad SaadaHaibin CaiPublished in: Neural Comput. Appl. (2023)
Keyphrases
- multi class
- error correcting output codes
- ensemble methods
- binary classification problems
- base classifiers
- binary classification
- support vector machine
- multi class classification
- single class
- binary classifiers
- multi class classifier
- cost sensitive
- decision trees
- majority voting
- probabilistic boosting tree
- machine learning methods
- multi class boosting
- binary and multi class
- multi class problems
- imbalanced data
- ensemble classifier
- multiple instance learning
- random forests
- feature selection
- class imbalance
- multi task
- classification accuracy
- generalization ability
- support vector
- pairwise
- prediction accuracy
- classifier ensemble
- benchmark datasets
- multiple classes
- support vector machine svm
- decision boundary
- cost sensitive learning
- logistic regression
- test data
- knn
- supervised learning
- text classification
- model selection
- k nearest neighbor
- training samples
- machine learning algorithms