A computationally fast variable importance test for random forests for high-dimensional data.
Silke JanitzaEnder CelikAnne-Laure BoulesteixPublished in: Adv. Data Anal. Classif. (2018)
Keyphrases
- high dimensional data
- random forests
- nearest neighbor
- regression problems
- random forest
- high dimensional
- dimensionality reduction
- low dimensional
- logistic regression
- decision trees
- ensemble methods
- subspace clustering
- dimension reduction
- machine learning algorithms
- data sets
- data points
- data analysis
- clustering high dimensional data
- manifold learning
- input space
- high dimensional datasets
- high dimensional spaces
- input data
- linear discriminant analysis
- image processing
- lower dimensional
- decision tree ensembles
- image classification
- data mining
- principal component analysis
- neural network