Group for Automation in Signals and Communications, Technical University of Madrid, ETSI Telecomunciación, Ciudad Universitaria, Madrid 28040, Spain.
Int J Neural Syst. 2011 Aug;21(4):311-7. doi: 10.1142/S0129065711002857.
The assessment of the risk of default on credit is important for financial institutions. Different Artificial Neural Networks (ANN) have been suggested to tackle the credit scoring problem, however, the obtained error rates are often high. In the search for the best ANN algorithm for credit scoring, this paper contributes with the application of an ANN Training Algorithm inspired by the neurons' biological property of metaplasticity. This algorithm is especially efficient when few patterns of a class are available, or when information inherent to low probability events is crucial for a successful application, as weight updating is overemphasized in the less frequent activations than in the more frequent ones. Two well-known and readily available such as: Australia and German data sets has been used to test the algorithm. The results obtained by AMMLP shown have been superior to state-of-the-art classification algorithms in credit scoring.
信用违约风险评估对金融机构很重要。已经提出了不同的人工神经网络（ANN）来解决信用评分问题，但是得到的错误率往往很高。在寻找用于信用评分的最佳 ANN 算法时，本文通过应用一种受神经元可塑性生物特性启发的 ANN 训练算法做出了贡献。当一类模式很少时，或者当低概率事件固有的信息对成功应用至关重要时，该算法尤其有效，因为与更频繁的激活相比，较少频繁的激活会过度强调权重更新。使用了两个著名的、易于获取的数据集，如：澳大利亚和德国数据集来测试该算法。由 AMMLP 得到的结果在信用评分方面优于最先进的分类算法。
