Coastal Wetland Mapping Using Ensemble Learning Algorithms: A Comparative Study of Bagging, Boosting and Stacking Techniques.
Li WenMichael HughesPublished in: Remote. Sens. (2020)
Keyphrases
- ensemble learning
- base classifiers
- base learners
- ensemble methods
- learning algorithm
- generalization ability
- decision stumps
- decision trees
- boosting algorithms
- ensemble classifier
- training samples
- weak learners
- learning machines
- multi class
- gradient boosting
- random forest
- bias variance analysis
- machine learning
- negative correlation learning
- machine learning methods
- naive bayes
- random forests
- unlabeled data
- machine learning algorithms
- ensemble members
- class distribution
- combining classifiers
- training data
- ensemble classification
- classifier ensemble
- prediction accuracy
- class labels
- learning scheme
- meta learning
- random subspace
- active learning
- training set
- learning problems
- cost sensitive
- test data
- randomized trees
- classification algorithm
- adaboost algorithm
- imbalanced data
- rotation forest
- multiple classifier systems
- weak classifiers
- combining multiple
- supervised learning
- benchmark datasets
- weighted voting
- back propagation
- concept drift
- accurate classifiers
- decision forest
- individual classifiers
- cluster ensemble
- labeled data
- majority voting