Random forest-based approach for physiological functional variable selection for driver's stress level classification.
Neska El HaouijJean-Michel PoggiRaja GhoziSylvie Sevestre-GhalilaMériem JaïdanePublished in: Stat. Methods Appl. (2019)
Keyphrases
- random forest
- variable selection
- decision trees
- cross validation
- fold cross validation
- feature set
- dimension reduction
- model selection
- random forests
- feature selection
- decision tree learning algorithms
- input variables
- classification accuracy
- high dimensional
- machine learning
- ensemble methods
- supervised learning
- support vector
- training set
- pattern recognition
- feature space
- feature ranking
- ensemble classifier
- support vector machine
- multi label
- nearest neighbor
- image classification
- feature vectors
- image processing
- feature extraction
- semi supervised
- training data
- machine learning methods
- benchmark datasets
- high dimensional data
- classification models
- similarity measure
- ensemble learning
- classification algorithm
- text mining
- naive bayes
- support vector machine svm