A novel multi-class imbalanced EEG signals classification based on the adaptive synthetic sampling (ADASYN) approach.
Adi AlhudhaifPublished in: PeerJ Comput. Sci. (2021)
Keyphrases
- multi class
- eeg signals
- multiclass classification
- single class
- binary classification problems
- multi class classifier
- multi class classification
- support vector machine
- binary classifiers
- binary and multi class
- multi class problems
- multiple classes
- multiclass problems
- feature selection
- cost sensitive
- multi class boosting
- signal processing
- error correcting output codes
- multi class svm
- motor imagery
- binary classification
- brain computer interface
- class imbalance
- multi class svms
- image classification
- classification accuracy
- classification algorithm
- eeg data
- minority class
- support vector
- base classifiers
- pattern recognition
- feature space
- kernel logistic regression
- protein classification
- pairwise
- machine learning
- binary classification tasks
- sampling methods
- image processing
- feature extraction
- multi label classification
- decision boundary
- concept drift
- svm classifier
- text classification
- feature set