Cost-sensitive ensemble methods for bankruptcy prediction in a highly imbalanced data distribution: a real case from the Spanish market.
Nazeeh GhatashehHossam FarisRuba AbukhurmaPedro A. CastilloNailah Al-MadiAntonio Miguel MoraAla' M. Al-ZoubiAhmad Basheer HassanatPublished in: Prog. Artif. Intell. (2020)
Keyphrases
- cost sensitive
- data distribution
- ensemble methods
- class distribution
- base learners
- base classifiers
- misclassification costs
- imbalanced data
- multi class
- cost sensitive learning
- decision trees
- naive bayes
- prediction accuracy
- machine learning methods
- ensemble learning
- decision boundary
- concept drift
- random forests
- data streams
- index structure
- data points
- high dimensional data
- active learning
- fraud detection
- random forest
- benchmark datasets
- class imbalance
- ensemble classifier
- support vector machine
- low dimensional
- data sets