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Classifying credit ratings for Asian banks using integrating feature selection and the CPDA-based rough sets approach

机译：使用功能选择和基于CPDA的粗糙集方法对亚洲银行的信用评级进行分类

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摘要

Although Asia is at the forefront of global economic growth, its investment environment is very risky and uncertain. Credit ratings are objective opinions about credit worthiness, investment risk, and default probabilities of issues or issuers. To classify credit ratings, analyze their determinants, and provide meaningful decision rules for interested parties, this work proposes an integrated procedure. First, this work adopts an integrated feature-selection approach to select key attributes, and then adopts an objective cumulative probability distribution approach (CPDA) to partition selected condition attributes by applying rough sets local-discretization cuts. This work then applies the rough sets LEM2 algorithm to generate a comprehensible set of decision rules. Finally, this work utilizes a rule filter to eliminate rules with poor support and thereby improve rule quality. The experimental focus was the Asian banking industry. Data were retrieved from a BankScope database that covers 1327 Asian banks. Experimental results demonstrate that the proposed procedure is an effective method of removing irrelevant attributes and achieving increased accuracy, providing a knowledge-based system for classification of rules for solving credit-rating problems encountered by banks, thereby benefiting interested parties.
机译：尽管亚洲处于全球经济增长的最前沿，但其投资环境仍然充满风险和不确定性。信用评级是关于信用价值，投资风险以及发行人或发行人的违约概率的客观观点。为了对信用等级进行分类，分析其决定因素并为感兴趣的各方提供有意义的决策规则，这项工作提出了一个综合程序。首先，这项工作采用集成的特征选择方法来选择关键属性，然后采用客观的累积概率分布方法（CPDA）通过应用粗糙集局部离散化分割来划分选定的条件属性。然后，这项工作将粗糙集LEM2算法应用于生成可理解的决策规则集。最后，这项工作利用规则过滤器来消除支持不佳的规则，从而提高规则质量。实验重点是亚洲银行业。从涵盖1327家亚洲银行的BankScope数据库中检索数据。实验结果表明，所提出的程序是一种去除无关属性并提高准确性的有效方法，为解决银行所遇到的信用评级问题的规则分类提供了一个基于知识的系统，从而使有关各方受益。

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