Boosting diversity in regression ensembles.
Mathias BourelJairo CugliariYannig GoudeJean-Michel PoggiPublished in: Stat. Anal. Data Min. (2024)
Keyphrases
- base learners
- ensemble methods
- ensemble learning
- base classifiers
- classifier ensemble
- ensemble members
- gradient boosting
- random forests
- diversity measures
- regression problems
- ensemble feature selection
- decision trees
- regression model
- tree induction
- regression trees
- negative correlation learning
- decision tree ensembles
- relevance vector machine
- benchmark datasets
- kernel ridge regression
- prediction accuracy
- cross validation
- classification and regression trees
- multi class
- random forest
- machine learning methods
- learning machines
- ensemble classifier
- learning algorithm
- multiple classifier systems
- weighted voting
- regression analysis
- generalization ability
- linear regression
- classification algorithm
- regression algorithm
- decision stumps
- ensemble selection
- model selection
- weak learners
- greedy search
- gaussian processes
- tree ensembles
- training data
- neural network ensemble
- partial least squares
- majority voting
- boosting algorithms
- weak classifiers
- cost sensitive
- regression methods
- imbalanced data
- regression method
- feature subset
- naive bayes
- genetic programming
- artificial neural networks
- word sense induction