Name
Title | ||
|---|---|---|
Improving the classification accuracy of RBF and MLP neural networks trained with imbalanced samples |
Abstract | ||
|---|---|---|
In practice, numerous applications exist where the data are imbalanced. It supposes a damage in the performance of the classifier. In this paper, an appropriate metric for imbalanced data is applied as a filtering technique in the context of Nearest Neighbor rule, to improve the classification accuracy in RBF and MLP neural networks. We diminish atypical or noisy patterns of the majority-class keeping all samples of the minority-class. Several experiments with these preprocessing techniques are performed in the context of RBF and MLP neural networks. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1007/11875581_56 | IDEAL |
Keywords | Field | DocType |
noisy pattern,mlp neural network,nearest neighbor rule,imbalanced sample,classification accuracy,numerous application,imbalanced data,preprocessing technique,nearest neighbor | k-nearest neighbors algorithm,Nearest neighbour,Weighted distance,Radial basis function,Pattern recognition,Computer science,Filter (signal processing),Preprocessor,Artificial intelligence,Classifier (linguistics),Artificial neural network,Machine learning | Conference |
Volume | ISSN | ISBN |
4224 | 0302-9743 | 3-540-45485-3 |
Citations | PageRank | References |
5 | 0.49 | 6 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| R. Alejo | 1 | 158 | 10.40 |
| V. García | 2 | 226 | 8.34 |
| J. M. Sotoca | 3 | 109 | 4.59 |
| Ramón A. Mollineda | 4 | 383 | 20.41 |
| José Salvador Sánchez | 5 | 565 | 31.62 |