Abstract is missing.
- 2nd Workshop on Learning with Imbalanced Domains: PrefaceLuís Torgo, Stan Matwin, Nathalie Japkowicz, Bartosz Krawczyk, Nuno Moniz, Paula Branco. 1-7 [doi]
- Learning from Positive and Unlabeled Data under the Selected At Random AssumptionJessa Bekker, Jesse Davis. 8-22 [doi]
- Multi-label kNN Classifier with Self Adjusting Memory for Drifting Data StreamsMartha Roseberry, Alberto Cano 0001. 23-37 [doi]
- Non-Linear Gradient Boosting for Class-Imbalance LearningJordan Fréry, Amaury Habrard, Marc Sebban, Liyun He-Guelton. 38-51 [doi]
- Proper Losses for Learning with Example-Dependent CostsAlexander Hepburn, Ryan McConville, Raúl Santos-Rodríguez, Jesús Cid-Sueiro, Dario García-García. 52-66 [doi]
- REBAGG: REsampled BAGGing for Imbalanced RegressionPaula Branco, Luís Torgo, Rita P. Ribeiro. 67-81 [doi]
- Undersampled Majority Class Ensemble for highly imbalanced binary classificationPawel Ksieniewicz. 82-94 [doi]
- ImWeights: Classifying Imbalanced Data Using Local and Neighborhood InformationMateusz Lango, Dariusz Brzezinski, Jerzy Stefanowski. 95-109 [doi]
- On the Need of Class Ratio Insensitive Drift Tests for Data StreamsAndré Gustavo Maletzke, Denis Moreira dos Reis, Everton Alvares Cherman, Gustavo E. A. P. A. Batista. 110-124 [doi]