Abstract is missing.
- 3rd Workshop on Learning with Imbalanced Domains: PrefaceNuno Moniz, Paula Branco, Luís Torgo, Nathalie Japkowicz, Michal Wozniak 0001, Shuo Wang. 1-6 [doi]
- Centralised vs decentralised anomaly detection: when local and imbalanced data are beneficialMirko Nardi, Lorenzo Valerio, Andrea Passarella. 7-20 [doi]
- Online-MC-Queue: Learning from Imbalanced Multi-Class StreamsFarnaz Sadeghi, Herna L. Viktor. 21-34 [doi]
- ML-NCA: Multi-label Neighbourhood Component AnalysisArjun Pakrashi, Payel Sadhukhan, Brian Mac Namee. 35-48 [doi]
- BayesBoost: Identifying and Handling Bias Using Synthetic Data GeneratorsBarbara Draghi, Zhenchen Wang, Puja Myles, Allan Tucker. 49-62 [doi]
- Learning to Rank Anomalies: Scalar Performance Criteria and Maximization of Two-Sample Rank StatisticsMyrto Limnios, Nathan Noiry, Stéphan Clémençon. 63-75 [doi]
- On Oversampling via Generative Adversarial Networks under Different Data Difficulty FactorsEhsan Nazari, Paula Branco. 76-89 [doi]
- Two Ways of Extending BRACID Rule-based Classifiers for Multi-class Imbalanced DataMaria Naklicka, Jerzy Stefanowski. 90-103 [doi]
- GanoDIP - GAN Anomaly Detection through Intermediate Patches: a PCBA Manufacturing CaseArnaud Bougaham, Adrien Bibal, Isabelle Linden, Benoît Frénay. 104-117 [doi]