Name
Abstract | ||
|---|---|---|
•The concept of mutual class potential is extended to the undersampling procedure.•Radial-Based Undersampling offers significantly lower computational complexity.•Method achieves significantly better results when combined with selected classifiers.•Areas of applicability of the algorithm are identified. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.patcog.2020.107262 | Pattern Recognition |
Keywords | Field | DocType |
Machine learning,Classification,Imbalanced data,Undersampling,Radial basis functions | Oversampling,Outlier,Undersampling,Data imbalance,Artificial intelligence,Data classification,Time complexity,Machine learning,Mathematics,Computational complexity theory | Journal |
Volume | Issue | ISSN |
102 | 1 | 0031-3203 |
Citations | PageRank | References |
4 | 0.38 | 0 |
Authors | ||
1 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Michal Koziarski | 1 | 18 | 3.66 |