Random forest kernel for high-dimension low sample size classification.
Lucca Portes CavalheiroSimon BernardJean Paul BarddalLaurent HeuttePublished in: Stat. Comput. (2024)
Keyphrases
- high dimension
- small sample
- sample size
- random forest
- feature space
- feature set
- decision trees
- model selection
- random forests
- random sampling
- input space
- feature selection
- real valued
- upper bound
- high dimensional
- support vector machine
- classification accuracy
- support vector
- high dimensionality
- classification algorithm
- ensemble classifier
- ensemble methods
- class labels
- data sets
- cross validation
- feature vectors
- kernel function
- training samples
- image classification
- multi label
- benchmark datasets
- high dimensional data
- classification models
- pairwise
- lower bound
- pattern recognition
- feature selection algorithms
- k nearest neighbor
- dimensionality reduction