Name
Abstract | ||
|---|---|---|
Recently, the class imbalance problem in neural networks, is receiving growing attention in works of machine learning and data mining. This problem appears when the samples of some classes are much smaller than those in the other classes. The classes with small size can be ignored in the learning process and the convergence of these classes is very slow. This paper studies empirically the class imbalance problem in the context of the RBF neural network trained with backpropagation algorithm. We propose to introduce a cost function in the training process to compensate imbalance class and one strategy to reduce the impact of the cost function in the data probability distribution. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1007/978-3-540-73007-1_20 | IWANN |
Keywords | Field | DocType |
rbf neural network,class imbalance problem,data mining,data probability distribution,neural network,imbalanced sample,paper study,backpropagation algorithm,cost function,imbalance class,training process,probability distribution,machine learning | Convergence (routing),Pattern recognition,Computer science,Mean squared error,Probability distribution,Artificial intelligence,Deep learning,Backpropagation,Artificial neural network,Machine learning | Conference |
Volume | ISSN | Citations |
4507 | 0302-9743 | 15 |
PageRank | References | Authors |
0.73 | 4 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| R. Alejo | 1 | 158 | 10.40 |
| Vicente García | 2 | 78 | 6.37 |
| J. M. Sotoca | 3 | 109 | 4.59 |
| Ramón A. Mollineda | 4 | 383 | 20.41 |
| José Salvador Sánchez | 5 | 565 | 31.62 |