Implementation of ensemble machine learning classifiers to predict diarrhoea with SMOTEENN, SMOTE, and SMOTETomek class imbalance approaches.
Elliot MbungeMaureen Nokuthula SibiyaSam TakavarashaRichard C. MillhamGarikayi ChemhakaBenhildah MuchemwaTafadzwa DzinamariraPublished in: ICTAS (2023)
Keyphrases
- class imbalance
- binary classification problems
- imbalanced data
- class distribution
- machine learning
- imbalanced data sets
- majority class
- minority class
- active learning
- feature selection
- sampling methods
- class imbalanced
- cost sensitive learning
- imbalanced datasets
- cost sensitive
- machine learning methods
- machine learning algorithms
- multiple classifier systems
- decision trees
- training set
- rare events
- learning algorithm
- training data
- support vector machine
- concept drift
- ensemble methods
- ensemble learning
- ensemble classifier
- semi supervised
- high dimensionality
- majority voting
- test set
- training examples
- change detection
- highly skewed
- supervised learning
- training samples
- text categorization
- classifier ensemble
- random forest
- roc curve
- classification error