Tree-Based Cost Sensitive Methods for Fraud Detection in Imbalanced Data.
Guillaume MetzlerXavier BadicheBrahim BelkasmiÉlisa FromontAmaury HabrardMarc SebbanPublished in: IDA (2018)
Keyphrases
- cost sensitive
- fraud detection
- cost sensitive learning
- class distribution
- imbalanced data
- cost sensitive classification
- multi class
- class imbalance
- misclassification costs
- benchmark datasets
- cost sensitive boosting
- naive bayes
- data mining techniques
- machine learning methods
- binary classification
- outlier detection
- semi supervised
- support vector machine
- base classifiers
- supervised learning