Enhancing Medicare Fraud Detection Through Machine Learning: Addressing Class Imbalance With SMOTE-ENN.
Bounab RayeneKarim ZarourBouchra GuelibNawres KhlifaPublished in: IEEE Access (2024)
Keyphrases
- fraud detection
- class imbalance
- cost sensitive
- machine learning
- cost sensitive learning
- class distribution
- active learning
- imbalanced datasets
- misclassification costs
- support vector machine
- imbalanced data sets
- rare events
- class imbalanced
- imbalanced data
- multi class
- minority class
- data mining
- feature selection
- outlier detection
- information extraction
- majority class
- sampling methods
- naive bayes
- data mining techniques
- supervised learning
- decision trees
- machine learning methods
- text mining
- social network analysis
- data analysis
- semi supervised learning
- text classification
- knowledge discovery
- high dimensional
- training set
- unlabeled data
- dimensionality reduction
- learning algorithm