Cluster-based under-sampling with random forest for multi-class imbalanced classification.
Md. Yasir ArafatSabera HoqueDewan Md. FaridPublished in: SKIMA (2017)
Keyphrases
- random forest
- decision trees
- feature set
- class imbalanced
- random forests
- classification accuracy
- feature selection
- feature space
- imbalanced data
- machine learning methods
- ensemble classifier
- ensemble methods
- image classification
- benchmark datasets
- class imbalance
- support vector machine svm
- svm classifier
- support vector machine
- machine learning
- feature extraction
- training set
- feature vectors
- supervised learning
- text classification
- classification algorithm
- multi label
- classification models
- base classifiers
- machine learning algorithms
- training samples
- knowledge discovery
- class labels
- unsupervised learning
- training dataset
- bayesian networks
- data mining