Classification of Cardiac Arrhythmia by Random Forests with Features Constructed by Kaizen Programming with Linear Genetic Programming.
Léo Françoso Dal Piccol SottoRegina Célia CoelhoVinícius Veloso de MeloPublished in: GECCO (2016)
Keyphrases
- random forests
- genetic programming
- feature set
- random forest
- decision trees
- regression problems
- feature vectors
- classification accuracy
- feature extraction
- machine learning algorithms
- classification models
- feature space
- tree ensembles
- classification and regression trees
- ecg signals
- regression forests
- accurate classifiers
- extracted features
- logistic regression
- support vector
- randomized trees
- ensemble methods
- benchmark datasets
- image features
- svm classifier
- feature selection
- learning algorithm
- feature subset
- machine learning
- heart rate variability
- machine learning methods
- classification algorithm
- supervised learning
- evolutionary algorithm
- training data
- prediction accuracy
- training examples
- support vector machine svm
- head pose estimation
- heart disease
- semi supervised
- knn
- mit bih arrhythmia database
- face recognition
- neural network