Handling Imbalanced Data in Churn Prediction Using RUSBoost and Feature Selection (Case Study: PT.Telekomunikasi Indonesia Regional 7).
Erna Dwiyanti AdiwijayaArie ArdiyantiPublished in: SCDM (2016)
Keyphrases
- neural network
- imbalanced data
- churn prediction
- random forest
- feature selection
- feature ranking
- ensemble learning
- information gain
- feature set
- ensemble methods
- ensemble classifier
- decision trees
- pattern recognition
- support vector machine
- classification models
- rule induction
- real world
- text categorization
- support vector
- linear regression
- base classifiers
- feature selection algorithms
- high dimensionality
- multi class
- classification accuracy
- class distribution
- prediction accuracy
- generalization ability
- data mining
- training data
- data analysis
- feature space
- class imbalance
- high dimensional
- concept drift
- nearest neighbor