EVALUATION AND MANAGEMENT OF CREDIT RISK USING COMPUTATIONAL INTELLIGENCE AND MULTI-OBJECTIVE DECISION MAKING

利用计算智能和多目标决策评估和管理信用风险

• 批准号: 13680540
• 负责人: NAKAYAMA Hirotaka
• 金额: $ 1.98万
• 依托单位: KONAN UNIVERSITY
• 依托单位国家: 日本
• 项目类别: Grant-in-Aid for Scientific Research (C)
• 财政年份: 2001
• 资助国家: 日本
• 起止时间: 2001 至 2003
• 项目状态: 已结题
• 来源: https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-13680540/
• 关键词: CREDIT RISK; SUPPORT VECTOR MACHINE; MULTI-OBJECTIVE PROGRAMMING; DATA ENVELOPMENT ANALYSIS; 能動忘却; サポートベクターマシン; RBFネットワーク; DEA

In financial activities such as investment and business finance, it is important to evaluate moderately credit risk against business failure of enterprises. The aim of this research is to construct a system for evaluating credit risk of enterprises using computational intelligence and for managing the risk to get as much profit as possible under some allowable risk by virtue,of multiple criteria decision making.Firstly, support vector machines(SVMs) were applied to evaluate credit risk of enterprises on the basis of qualitative and quantitative data sets. Those data sets for business failure have some unbalance : failure data are only a few percentage, and almost of all date are of nonfailure. In order to overcome this problem, SVMs were modified by using multi-objective programming and/or goal programming. As a result, the modified SVM showed a good classification ability for the category with extremely fewer elements. Moreover, the rough set theory was applied to extract simple and e … More xplicit rules from the obtained support vectors.Secondly, dynamically adapting learning machines for the change of environment were developed. Learning machines can increase their ability by making incremental learning. If we make only incremental learning, however, the decision rule becomes more and more complex, which resluts in poor generalization. Therefore, it is needed to remove unnecessary(or obstacle) data under the present situation. This is called "forgetting". In this research were developed several methods for forgetting not only in a passive manner in which the influence of data decreases over time but also in an active way in which unnecessary(obstacle) data are found and removed actively.If we only avoid risk, we can not make an active financial activities, because every financial activitiy has some risk. Finally, therefore, data envelopment analysis(DBA) was applied to evaluate the efficiency of decision unit in order to get as much profit as possible under some allowable risk. Since the conventional DEA is based on the convex hull of data set taking into account the linear value judgment, it can not be applied to problems under nonlinear value judgment. A generalized DEA was developed to attempt to measure the efficiency of decision unit under several kinds of nonlinear value judgments. The effectiveness of the generalized DEA was proved through several examples. Less

在投资、企业融资等金融活动中，对企业经营失败的信用风险进行适度评估是非常重要的。本文的研究目的是构建一个基于计算智能的企业信用风险评估系统，并利用多准则决策方法对企业信用风险进行管理，使企业在一定的风险允许范围内获得尽可能多的利润。企业失败数据集存在一定的不平衡性：失败数据只占很小的比例，而且几乎所有的数据都是非失败数据。为了克服这个问题，支持向量机修改，使用多目标规划和/或目标规划。结果表明，改进后的支持向量机对元素数量极少的类别具有良好的分类能力。此外，还应用粗糙集理论提取了简单的和复杂的特征， ...更多信息 其次，提出了对环境变化进行动态适应的学习机。学习机器可以通过增量学习来提高它们的能力。然而，如果我们只进行增量学习，决策规则变得越来越复杂，这导致泛化能力差。因此，在目前的情况下，需要去除不必要的（或障碍）数据。这叫做“遗忘”。在本研究中，我们开发了几种遗忘方法，既可以以被动的方式，即数据的影响随着时间的推移而减少，也可以以主动的方式，即主动地发现和删除不必要的（障碍）数据。如果我们只是回避风险，我们不能使一个积极的财务活动，因为每一个财务活动都有一定的风险。最后，采用数据挖掘分析（DBA）方法对决策单元的效率进行评估，以在一定的风险允许范围内获得尽可能多的收益。由于传统的DEA是基于数据集的凸船体考虑线性价值判断，它不能适用于非线性价值判断的问题。提出了一种广义DEA方法，试图度量决策单元在几种非线性价值判断下的效率。通过实例验证了广义DEA方法的有效性。少

项目成果

H.Nakayama, Y.Yun, et al.: "Goal Programming Approaches to Support Vector Machines"Proc. of KES03. 356-363 (2003)

H.Nakayama，Y.Yun，等：“支持向量机的目标编程方法”Proc。

H.Nakayama, M.Arakawa, R.Sasaki: "Optimization with Unknown Objective Functions using Computational Intelligence -A Comparative Study with RSM"Proc.of 5^<th> International Conference on Optimization : Technology and Applications Hong Kong(Ed.D.Li). 1163-1

H.Nakayama、M.Arakawa、R.Sasaki：“利用计算智能优化未知目标函数 - 与 RSM 的比较研究”Proc.of 5^<th> 国际优化会议：技术与应用香港（Ed.D）

M.OKamoto, Y.Araki, H.Nakayama, et al.: "A Study on the Extraction of Major Factors and Certain Laws of Sediment Transport Phenomenon by Applying the Rough Set Theory for Daa Mining"Journal of the Japan Society of Erosion Control Engineering(in Japanese).

M.OKamoto、Y.Araki、H.Nakayama 等：“应用粗集理论进行泥沙输送现象的主要因素提取和某些规律的研究”日本侵蚀控制学会会刊

H.Nakayama, A.Hattori: "Incremental Learning and Forgetting in RBF Networks and SVMs(Knowledge-Bases Intelligent Information and Engineering Systems)((Eds.) V.Palade, R.J.Hewlett and L.Jain)"Springer. 1109-1115 (2003)

H.Nakayama、A.Hattori：“RBF 网络和 SVM（基于知识的智能信息和工程系统）中的增量学习和遗忘（（编辑）V.Palade、R.J.Hewlett 和 L.Jain）”Springer。

荒川雅生, 中山弘隆, 石川浩: "ラディアルベーシス関数ネットワークと領域適応型遺伝的アルゴリズムを用いた最適設計(第2報 制約条件のない場合における検討)"日本機械学会論文集. 67・655. 797-802 (2001)

Masao Arakawa、Hirotaka Nakayama、Hiroshi Ishikawa：“使用径向基函数网络和域自适应遗传算法的优化设计（第二次报告：无约束情况下的研究）”日本机械工程师学会汇刊 67, 655. 797。 -802 (2001)