Customizing SVM as a base learner with AdaBoost ensemble to learn from multi-class problems: A hybrid approach AdaBoost-MSVM.
Zafar MehmoodSohail AsgharPublished in: Knowl. Based Syst. (2021)
Keyphrases
- base learners
- base classifiers
- ensemble methods
- multi class
- binary classification problems
- multi class problems
- ensemble learning
- decision stumps
- classification algorithm
- weak classifiers
- generalization ability
- random forests
- training data
- cost sensitive
- meta learning
- decision trees
- learning scheme
- regression problems
- ensemble classifier
- support vector machine
- training process
- multi class classification
- naive bayes
- loss function
- prediction accuracy
- training set
- support vector
- boosting algorithms
- random forest
- learning algorithm
- support vector machine svm
- classifier ensemble
- learning tasks
- feature selection
- cross validation
- logistic regression
- binary classification
- multiclass classification
- benchmark datasets
- machine learning methods
- knn
- classification error
- multi task
- class imbalance
- class distribution
- linear regression
- majority voting
- test data
- k nearest neighbor
- linear combination
- machine learning
- learning problems
- mean shift
- class labels