Experimental evaluation of the accuracy of an ensemble of fuzzy methods for classification of episodes in bipolar disorder.
Katarzyna Kaczmarek-MajerAdam KiersztynPublished in: FUZZ-IEEE (2022)
Keyphrases
- experimental evaluation
- machine learning methods
- classification accuracy
- classification method
- preprocessing
- computational cost
- classification systems
- benchmark datasets
- publicly available data sets
- training set
- feature extraction
- individual classifiers
- decision trees
- positive and negative
- classifier ensemble
- accuracy rate
- synthetic and real datasets
- majority voting
- feature vectors
- ensemble methods
- roc analysis
- fuzzy sets
- fuzzy logic
- cross validation
- fuzzy classification
- fuzzy decision trees
- image classification
- neural network
- multiple classifier systems
- classification performances
- regression problems
- machine learning
- random forests
- generalization ability
- classification models
- benchmark data sets
- training data
- prediction accuracy
- machine learning algorithms
- support vector