Bagging based ensemble of Support Vector Machines with improved elitist GA-SVM features selection for cardiac arrhythmia classification.
Vinod Jagannath KadamShivajirao Manikrao JadhavSamir S. YadavPublished in: Int. J. Hybrid Intell. Syst. (2020)
Keyphrases
- ensemble classifier
- svm classifier
- decision tree classifiers
- feature vectors
- imbalanced data
- support vector machine svm
- training set
- classification method
- classification accuracy
- feature space
- ensemble methods
- ensemble learning
- support vector machine
- majority voting
- svm classification
- support vector
- classification models
- feature set
- feature selection
- random forest
- ensemble classification
- feature extraction
- generalization ability
- decision forest
- decision trees
- classifier ensemble
- weak classifiers
- support vector machine classifiers
- machine learning
- classification algorithm
- random subspace
- evolutionary algorithm
- feature ranking
- training data
- extracted features
- decision stumps
- class labels
- weak learners
- ecg signals
- base classifiers
- heart rate variability
- input features
- feature subset
- train a support vector machine
- ensemble selection
- genetic algorithm
- random forests
- tree ensembles
- benchmark datasets
- feature reduction
- single feature
- sequential forward selection
- prediction accuracy
- neural network
- multiple classifier systems
- multi objective
- model selection
- multi class classification
- multiple kernel learning
- regression problems
- multiple features
- individual features
- individual classifiers
- concept drift
- ensemble members
- text classification
- fitness function
- immune genetic algorithm
- kernel function