Cost-sensitive and hybrid-attribute measure multi-decision tree over imbalanced data sets.
Fenglian LiXueying ZhangXiqian ZhangChunlei DuYue XuYu-Chu TianPublished in: Inf. Sci. (2018)
Keyphrases
- cost sensitive
- class distribution
- imbalanced data sets
- decision trees
- naive bayes
- class imbalance
- misclassification costs
- cost sensitive learning
- multi class
- binary classification
- similarity measure
- active learning
- fraud detection
- minority class
- imbalanced data
- base classifiers
- text classification
- training set
- attribute values
- machine learning
- machine learning algorithms
- decision rules
- support vector machine
- classification trees
- classification accuracy
- feature selection
- ensemble methods
- evaluation measures
- pairwise
- training data
- learning algorithm