The effects of varying class distribution on learner behavior for medicare fraud detection with imbalanced big data.
Richard A. BauderTaghi M. KhoshgoftaarPublished in: Health Inf. Sci. Syst. (2018)
Keyphrases
- fraud detection
- big data
- class distribution
- cost sensitive
- rare events
- class imbalance
- imbalanced datasets
- highly skewed
- misclassification costs
- multi class
- imbalanced data sets
- cloud computing
- cost sensitive learning
- imbalanced data
- data analysis
- data management
- outlier detection
- highly imbalanced
- minority class
- data processing
- social media
- e learning
- data mining techniques
- active learning
- social network analysis
- training set
- knowledge discovery
- naive bayes
- business intelligence
- learning environment
- training data
- data warehousing
- decision trees
- data mining
- base learners
- training samples
- test set
- database
- data warehouse
- support vector machine
- learning process
- pairwise
- databases
- data sets