MetaAP: A meta-tree-based ranking algorithm optimizing the average precision from imbalanced data.
Rémi ViolaLéo GautheronAmaury HabrardMarc SebbanPublished in: Pattern Recognit. Lett. (2022)
Keyphrases
- ranking algorithm
- average precision
- imbalanced data
- retrieval effectiveness
- test collection
- linear regression
- retrieval systems
- evaluation metrics
- ensemble methods
- class distribution
- vector space model
- ranked list
- decision trees
- feature selection
- class imbalance
- support vector machine
- sampling methods
- query expansion
- ranking functions
- information retrieval