Name
Title | ||
|---|---|---|
A hybrid method to face class overlap and class imbalance on neural networks and multi-class scenarios |
Abstract | ||
|---|---|---|
Class imbalance and class overlap are two of the major problems in data mining and machine learning. Several studies have shown that these data complexities may affect the performance or behavior of artificial neural networks. Strategies proposed to face with both challenges have been separately applied. In this paper, we introduce a hybrid method for handling both class imbalance and class overlap simultaneously in multi-class learning problems. Experimental results on five remote sensing data show that the combined approach is a promising method. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1016/j.patrec.2012.09.003 | Pattern Recognition Letters |
Keywords | Field | DocType |
hybrid method,data mining,data complexity,promising method,major problem,machine learning,multi-class scenario,combined approach,class imbalance,artificial neural network,back propagation,cost function | Computer science,Artificial intelligence,Backpropagation,Artificial neural network,Machine learning | Journal |
Volume | Issue | ISSN |
34 | 4 | 0167-8655 |
Citations | PageRank | References |
17 | 0.61 | 26 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| R. Alejo | 1 | 158 | 10.40 |
| R. M. Valdovinos | 2 | 193 | 13.67 |
| V. García | 3 | 226 | 8.34 |
| J. H. Pacheco-Sanchez | 4 | 20 | 2.02 |