An improved bagging ensemble surrogate-assisted evolutionary algorithm for expensive many-objective optimization.
Qinghua GuXiaoyue ZhangLu ChenNaixue XiongPublished in: Appl. Intell. (2022)
Keyphrases
- evolutionary algorithm
- ensemble methods
- ensemble learning
- base classifiers
- multi objective
- multiple objectives
- ensemble selection
- ensemble classification
- ensemble members
- random forests
- random forest
- imbalanced data
- nsga ii
- classifier ensemble
- ensemble classifier
- optimization problems
- majority voting
- multi objective optimization
- evolutionary computation
- decision trees
- class label noise
- fitness function
- prediction accuracy
- training set
- randomized trees
- simulated annealing
- benchmark datasets
- generalization ability
- machine learning methods
- multi objective evolutionary algorithms
- differential evolution
- decision tree ensembles
- genetic programming
- decision tree classifiers
- mutation operator
- neural network
- bootstrap sampling
- selection strategy
- base learners
- genetic algorithm
- function optimization
- tree ensembles
- weak learners
- multi class
- support vector machine
- machine learning algorithms
- pareto optimal
- particle swarm optimization
- learning algorithm
- concept drift
- cost sensitive
- feature selection
- generalization error
- optimization algorithm
- training samples