Name
Abstract | ||
|---|---|---|
•A simple yet effective oversampling ensemble method for imbalanced data is proposed.•The sensitivity measure can compute impacts of class imbalance on training data.•Theoretical analyses for data generation are provided.•Proposed method is effective in handling datasets with various imbalance ratios.•The finding is ensembling perturbation-based oversamplers shows promising results. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1016/j.neucom.2022.01.049 | Neurocomputing |
Keywords | DocType | Volume |
Class imbalance,Ensemble learning,Oversampling,Perturbation,Sensitivity | Journal | 479 |
ISSN | Citations | PageRank |
0925-2312 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jianjun Zhang | 1 | 9 | 3.48 |
| Ting Wang | 2 | 0 | 0.34 |
| Wing W. Y. Ng | 3 | 0 | 0.34 |
| W. Pedrycz | 4 | 13966 | 1005.85 |