Improving the Classification Accuracy of RBF and MLP Neural Networks Trained with Imbalanced Samples-Reference-Cited by-同舟云学术

Improving the Classification Accuracy of RBF and MLP Neural Networks Trained with Imbalanced Samples

Published:2006 Issue: Volume: Page:464-471
ISSN:0302-9743
Container-title:Intelligent Data Engineering and Automated Learning – IDEAL 2006
language:
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Author:

Alejo R.,Garcia V.,Sotoca J. M.,Mollineda R. A.,Sánchez J. S.

Publisher

Springer Berlin Heidelberg

Link

http://link.springer.com/content/pdf/10.1007/11875581_56

Reference13 articles.

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2. Barandela, R., Cortés, N., Palacios, A.: The nearest neighbour rule and the reduction of the training sample size. In: 9th. Spanish Symposium on Pattern Recognition and Image Analysis, Benicassim, Spain, vol. 1, pp. 103–108 (2001)

3. Barandela, R., Sánchez, J.S., García, V., Rangel, E.: Strategies for learning in class imbalance problems. Pattern Recognition 36(3), 849–851 (2003)

4. Lecture Notes in Computer Science;R. Barandela,2004

5. Berardi, V.L., Zhang, G.P.: The Effect of Misclassification Costs on Neural Network Classifiers. Decision Sciences 30(3), 659–682 (1999)

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