【作者】 寇刚；

【Author】 GANG Kou ,YI Peng ,YONG SHI, ZHENG Xin-chen (College of Information Science & Technology, University of Nebraska at Omaha, Omaha, NE 68182 , USA; Chinese Academy of Sciences Research Center on Data Technology & Knowledge Economy, Graduate University of the Chinese Academy of Sciences,Beijing 100080,China)

【机构】 美国内布拉斯加州立大学信息科学与技术学院； 中国科学院数据挖掘与知识经济中心；

【摘要】 在银行以及金融相关产业,信用风险分析已日渐重要。当前基于数学规划(Mathematical programming)的分析方法已成为信用风险分析的有力工具。寻找全局最优解所需要面对的计算复杂度是数学规划在应用上的一个难题。本文提倡了一种新的多目标凸二次规划模型(Multi-criteria Convex Quadratic Programming model)(MCQP), 其最优解能通过线形行列式变换得到,大大降低了算法复杂度。我们对使用本模型得到的结果和4种知名的算法或工具(LDA,Decision Tree,SVMLight,和LibSVM)在4个来自不同国家(德国、澳大利亚、日本和美国)的信用数据集的计算结果进行了比较。试验结果表明,本方法的在2个数据集上最好,在另两个数据集上接近最好。该模型在中国的信用数据分析上将会有很好的应用前景。更多还原

【Abstract】 Mathematical programming based methods have been applied to credit risk analysis and have proven to be powerful tools. One challenging issue in mathematical programming is the computation complexity in finding optimal solutions. This paper proposes a Multi-criteria Convex Quadratic Programming model ( MCCQP). Instead of looking for the global optimal solution,the proposed model only needs to solve a set of linear equations. We test the model using three credit risk analysis datasets and compare MCCQP results with four well-known classification methods; LDA, Decision Tree, SVMLight, and LibSVM. The experimental results indicate the the proposed MCCQP model achieves as good as or even better classification accuracies than other methods.更多还原