Study of Hellinger Distance as a splitting metric for Random Forests in balanced and imbalanced classification datasets.
Ricardo AlerJosé María VallsHenrik BoströmPublished in: Expert Syst. Appl. (2020)
Keyphrases
- random forests
- decision trees
- machine learning algorithms
- accurate classifiers
- benchmark datasets
- classification accuracy
- regression forests
- imbalanced data
- ensemble methods
- tree ensembles
- support vector
- image classification
- feature extraction
- distance measure
- feature vectors
- classification and regression trees
- random forest
- nearest neighbor
- multi class
- feature space
- class labels
- logistic regression
- feature selection
- text classification
- feature set
- class imbalance
- classification trees
- supervised learning
- imbalanced datasets
- randomized trees
- support vector machine