Abstract is missing.
- Learning with Imbalanced Domains: PrefaceLuís Torgo, Bartosz Krawczyk, Paula Branco, Nuno Moniz. 1-6 [doi]
- Influence of minority class instance types on SMOTE imbalanced data oversamplingPrzemyslaw Skryjomski, Bartosz Krawczyk. 7-21 [doi]
- A Network Perspective on Stratification of Multi-Label DataPiotr Szymanski, Tomasz Kajdanowicz. 22-35 [doi]
- SMOGN: a Pre-processing Approach for Imbalanced RegressionPaula Branco, Luís Torgo, Rita P. Ribeiro. 36-50 [doi]
- Stacked-MLkNN: A stacking based improvement to Multi-Label k-Nearest NeighboursArjun Pakrashi, Brian Mac Namee. 51-63 [doi]
- Sampling a Longer Life: Binary versus One-class classification RevisitedColin Bellinger, Shiven Sharma, Osmar R. Zaïane, Nathalie Japkowicz. 64-78 [doi]
- Improving Resampling-based Ensemble in Churn PredictionBing Zhu, Seppe Vanden Broucke, Bart Baesens, Sebastián Maldonado. 79-91 [doi]
- Predicting Defective Engines using Convolutional Neural Networks on Temporal Vibration SignalsNikou Günnemann, Jürgen Pfeffer. 92-102 [doi]
- Effect of Data Imbalance on Unsupervised Domain Adaptation of Part-of-Speech Tagging and Pivot Selection StrategiesXia Cui, Frans Coenen, Danushka Bollegala. 103-115 [doi]
- Tunable Plug-In Rules with Reduced Posterior Certainty Loss in Imbalanced DatasetsEmmanouil Krasanakis, Eleftherios Spyromitros Xioufis, Symeon Papadopoulos, Yiannis Kompatsiaris. 116-128 [doi]
- Evaluation of Ensemble Methods in Imbalanced Regression TasksNuno Moniz, Paula Branco, Luís Torgo. 129-140 [doi]
- Controlling Imbalanced Error in Deep Learning with the Log Bilinear LossYehezkel S. Resheff, Amit Mandelbom, Daphna Weinshall. 141-151 [doi]
- Unsupervised Classification of Speaker Profiles as a Point Anomaly Detection TaskCedric Fayet, Arnaud Delhay, Damien Lolive, Pierre-François Marteau. 152-163 [doi]
- Dealing with the task of imbalanced, multidimensional data classification using ensembles of exposersPawel Ksieniewicz, Michal Wozniak. 164-175 [doi]