Naive random subspace ensemble with linear classifiers for real-time classification of fMRI data.
Catrin O. PlumptonLudmila I. KunchevaNikolaas N. OosterhofStephen J. JohnstonPublished in: Pattern Recognit. (2012)
Keyphrases
- random subspace
- random forest
- classifier fusion
- linear classifiers
- ensemble methods
- subspace methods
- random sampling
- majority voting
- nearest neighbor classifiers
- classifier ensemble
- ensemble learning
- random forests
- support vector machine
- ensemble classifier
- pattern recognition
- classification accuracy
- hyperparameters
- base classifiers
- svm classifier
- decision trees
- machine learning methods
- machine learning
- feature selection
- multi class
- classification algorithm
- feature subspace
- generalization ability
- cross validation
- neural network
- supervised learning
- similarity measure
- training data
- support vector
- feature space
- training set
- image features
- base learners
- hyperplane
- feature set
- image classification
- support vector machine svm
- machine learning algorithms
- prediction accuracy
- class labels
- data sets
- cost sensitive