A Preprocessing Approach for Class-Imbalanced Data Using SMOTE and Belief Function Theory.
Fares GrinaZied ElouediEric LefevrePublished in: IDEAL (2) (2020)
Keyphrases
- belief functions
- preprocessing
- dempster shafer
- probability theory
- dempster shafer theory
- class imbalanced data
- imprecise probabilities
- uncertain reasoning
- evidential reasoning
- probability function
- handling uncertainty
- utility theory
- class distribution
- bayesian networks
- probability functions
- multicriteria decision making
- markov tree
- decision makers
- expected utility
- active learning
- feature extraction
- machine learning