MOCA-I: Discovering Rules and Guiding Decision Maker in the Context of Partial Classification in Large and Imbalanced Datasets.
Julie JacquesJulien TaillardDavid DelerueLaetitia JourdanClarisse DhaenensPublished in: LION (2013)
Keyphrases
- decision makers
- imbalanced datasets
- cost sensitive learning
- class imbalance
- decision making
- utility function
- classification rules
- rule extraction
- decision rules
- support vector machine
- feature selection algorithms
- ensemble methods
- rule sets
- cost sensitive
- machine learning
- class distribution
- support vector
- classification accuracy
- benchmark datasets
- decision trees
- data sets
- training dataset
- feature selection
- imbalanced data
- feature extraction
- training set
- classification algorithm
- probability estimation