An investigation of smote based methods for imbalanced datasets with data complexity analysis
NA Azhar, MSM Pozi, AM Din… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Many binary class datasets in real-life applications are affected by class imbalance problem.
Data complexities like noise examples, class overlap and small disjuncts problems are …
Data complexities like noise examples, class overlap and small disjuncts problems are …
Dynamic Classifier Selection for Data with Skewed Class Distribution Using Imbalance Ratio and Euclidean Distance
P Zyblewski, M Woźniak - … , Amsterdam, The Netherlands, June 3–5, 2020 …, 2020 - Springer
Imbalanced data analysis remains one of the critical challenges in machine learning. This
work aims to adapt the concept of Dynamic Classifier Selection (dcs) to the pattern …
work aims to adapt the concept of Dynamic Classifier Selection (dcs) to the pattern …
Clustering-Based Ensemble Pruning in the Imbalanced Data Classification
P Zyblewski - International Conference on Computational Science, 2021 - Springer
Ensemble methods in combination with data preprocessing techniques are one of the most
used approaches to dealing with the problem of imbalanced data classification. At the same …
used approaches to dealing with the problem of imbalanced data classification. At the same …