Name
Abstract | ||
|---|---|---|
One of the most encountered problems in the training of artificial neural networks is imbalanced datasets. It is common to apply oversampling algorithms to overcome the adverse affects of imbalanced datasets in classification performance. In this study, a new oversampling method based on Generative Adversarial Nets (GAN) and Edited K-Nearest Neighbor (KNN) is proposed. It is observed that the proposed method increases the classification performance more than that of the oversampling techniques based on the conventional SMOTE algorithm. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/SIU49456.2020.9302164 | 2020 28th Signal Processing and Communications Applications Conference (SIU) |
Keywords | DocType | ISSN |
Imbalanced Dataset,Generative Adversarial Nets,Machine Learning,Electronic Warfare | Conference | 2165-0608 |
ISBN | Citations | PageRank |
978-1-7281-7207-1 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Emirhan Özmen | 1 | 0 | 0.68 |
| Fuat Çogun | 2 | 0 | 0.68 |
| Fatih Altiparmak | 3 | 39 | 5.56 |