Multi-split optimized bagging ensemble model selection for multi-class educational data mining.
MohammadNoor InjadatAbdallah MoubayedAli Bou NassifAbdallah ShamiPublished in: Appl. Intell. (2020)
Keyphrases
- multi class
- model selection
- base classifiers
- educational data mining
- meta learning
- ensemble learning
- feature selection
- learning analytics
- cross validation
- ensemble methods
- generalization error
- random forest
- hyperparameters
- student modeling
- machine learning
- sample size
- regression model
- binary classification
- cost sensitive
- support vector machine
- statistical learning
- base learners
- learning machines
- pairwise
- random forests
- multi class classification
- binary classification problems
- majority voting
- training set
- gaussian process
- boosting algorithms
- binary classifiers
- decision trees
- benchmark datasets
- information criterion
- variable selection
- prediction accuracy
- generalization ability
- bayesian information criterion
- text classification
- classification accuracy
- linear classifiers
- machine learning methods
- multi task