Explainable Matrix - Visualization for Global and Local Interpretability of Random Forest Classification Ensembles.
Mário Popolin NetoFernando V. PaulovichPublished in: IEEE Trans. Vis. Comput. Graph. (2021)
Keyphrases
- random forest
- decision trees
- random subspace
- random forests
- ensemble methods
- fold cross validation
- feature set
- ensemble classifier
- cancer classification
- decision tree learning algorithms
- classification accuracy
- feature ranking
- imbalanced data
- ensemble learning
- rotation forest
- base classifiers
- machine learning methods
- prediction accuracy
- machine learning
- classifier ensemble
- feature importance
- machine learning algorithms
- support vector machine
- feature vectors
- classification models
- feature selection
- generalization ability
- classification algorithm
- logistic regression
- training data
- multi label
- naive bayes
- support vector machine svm
- training set
- class labels
- benchmark datasets
- majority voting
- training samples
- classifier combination
- unsupervised learning
- image classification
- supervised learning