Predicting Insolvency of Insurance Companies in Egyptian Market Using Bagging and Boosting Ensemble Techniques.
Ahmed A. KhalilZaiming LiuAhmad SalahAhmed FathallaAhmed AliPublished in: IEEE Access (2022)
Keyphrases
- ensemble methods
- ensemble learning
- base classifiers
- market share
- ensemble classification
- base learners
- ensemble classifier
- randomized trees
- random forests
- random forest
- decision tree ensembles
- ensemble members
- majority voting
- decision trees
- negative correlation learning
- small and medium sized
- rotation forest
- weak learners
- using artificial neural networks
- generalization ability
- machine learning methods
- prediction accuracy
- market segments
- benchmark datasets
- random subspace
- manufacturing companies
- predictive model
- competitive market
- classifier ensemble
- small businesses
- weak classifiers
- multi class
- imbalanced data
- weighted voting
- boosting algorithms
- external factors
- strategic decisions
- training set
- gradient boosting
- risk management
- decision stumps
- error function
- naive bayes
- ensemble selection
- stock market
- cost sensitive
- stock price
- meta learning
- learning algorithm
- class labels
- competitive advantage
- training samples
- feature set
- artificial neural networks
- feature selection