An Empirical Evaluation of Stacked Ensembles With Different Meta-Learners in Imbalanced Classification.
Seng ZianSameem Abdul KareemKasturi Dewi VarathanPublished in: IEEE Access (2021)
Keyphrases
- imbalanced data
- decision trees
- pattern recognition
- feature vectors
- classification accuracy
- machine learning
- pattern classification
- learning environment
- training set
- learning systems
- multiple classifier systems
- class imbalance
- machine learning methods
- feature extraction
- feature space
- language learning
- imbalanced data sets
- learning algorithm
- majority voting
- classifier ensemble
- support vector
- learning resources
- data sets
- text classification
- image classification
- classification method
- learning outcomes
- class labels
- training data
- ensemble learning
- classification systems
- e learning
- classifier combination
- supervised learning
- single class
- support vector machine