Improving the performance of the RBF neural networks trained with imbalanced samples

被引：0
|
作者：
Alejo, R. [1 ,2 ]
Garcia, V. [1 ,2 ]
Sotoca, J. M. [1 ]
Mollineda, R. A. [1 ]
Sanchez, J. S. [1 ]
机构：
[1] Univ Jaume 1, Dept Llenguatges & Sistemes Informat, Ave Sos Baynat S-N, Castellon De La Plana 12071, Spain
[2] Inst Tecnol Toluca, Lab Reconocimiento Patrones, Metepec 52140, Mexico
来源：
COMPUTATIONAL AND AMBIENT INTELLIGENCE | 2007年 / 4507卷
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
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.
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页码：162 / +
页数：2
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