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.
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Alejo, R., Garcia, V., Sotoca, J.M., Mollineda, R.A., Sánchez, J.S. (2006). Improving the Classification Accuracy of RBF and MLP Neural Networks Trained with Imbalanced Samples. In: Corchado, E., Yin, H., Botti, V., Fyfe, C. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2006. IDEAL 2006. Lecture Notes in Computer Science, vol 4224. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11875581_56
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DOI: https://doi.org/10.1007/11875581_56
Publisher Name: Springer, Berlin, Heidelberg
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