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首页> 外文会议>2011 Seventh International Conference on Natural Computation >Credit scoring model based on selective neural network ensemble
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Credit scoring model based on selective neural network ensemble

机译：基于选择性神经网络集成的信用评分模型

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Credit scoring has gained increasing attentions from banks, which can benefit from reducing possible risks of default. Based on the analysis of relationship between the performance of ensemble model and that of base classifiers, this paper proposes a selective neural network ensemble model for credit scoring, In which Artificial neural networks and ensemble learning methods are firstly employed to build a base classifiers pool, then hierarchical clustering algorithm is used to divide those base classifiers into several clusters, then the classifiers with highest accuracy in each cluster are chose to vote for the final decision. Three real world credit datasets are selected as the experimental data to demonstrate the accuracy of the model. The results show that selective neural network ensemble model can significantly improved the efficiency in selection of base classifiers and generalization ability and thereby show enough attractive features for credit risk management system.
机译：信用评分越来越受到银行的关注，这可以从减少违约风险中受益。在分析集成模型性能与基础分类器性能之间的关系的基础上，提出了一种用于信用评分的选择性神经网络集成模型，该模型首先采用人工神经网络和集成学习方法建立了基础分类器库，然后使用层次聚类算法将这些基本分类器划分为几个聚类，然后选择每个聚类中精度最高的分类器对最终决策进行投票。选择了三个真实世界的信用数据集作为实验数据，以证明该模型的准确性。结果表明，选择性神经网络集成模型可以显着提高基本分类器的选择效率和泛化能力，从而为信用风险管理系统显示出足够吸引人的特征。

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