RBF神经网络非对称损失改进及应用

针对应用RBF（Radial Basis Function）神经网络信用评分中存在的第Ⅰ类错误率高的问题，提出了基于Linex损失下RBF神经网络分类方法，并给出了UCI（University of California Irvine）中德国信用评分数据集上的测试结果。实验结果表明，该方法能有效解决传统RBF神经网络信用评分中存在的问题。

Improving RBF Neural Network under Asymmetric Loss and Its Application

Aiming at the problem in the application of the RBF（Radial Basis Function） neural network to the credit score, we propose a RBF neural network classification under the Linex loss function, and produce the test results on the Germany credit score data sets among the UCI（University of California Irvine）. The results illuminate that the method proposed in this paper may effectively solve the problem in the application of the RBF neural network to the credit score.