在不平衡数据上训练RBF神经网络

Proceedings of the 9th International Conference on Neural Information Processing, 2002. ICONIP '02. Pub Date : 2002-11-18 DOI:10.1109/ICONIP.2002.1198214

Xiuju Fu, Lipo Wang, K. Chua, Feng Chu

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引用次数: 31

摘要

本文提出了一种构造和训练非平衡RBF神经网络的新算法。在应用程序中，数据集中经常出现样本少得多的少数类。神经网络的学习过程通常偏向于拥有多数人口的班级。我们的研究重点是在保持整体分类性能的同时，提高少数类的分类精度。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Training RBF neural networks on unbalanced data

This paper presents a new algorithm for the construction and training of an RBF neural network with unbalanced data. In applications, minority classes with much fewer samples are often present in data sets. The learning process of a neural network usually is biased towards classes with majority populations. Our study focused on improving the classification accuracy of minority classes while maintaining the overall classification performance.

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来源期刊

Proceedings of the 9th International Conference on Neural Information Processing, 2002. ICONIP '02.

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