摘要
针对两类样本企业信用状况的重叠问题,提出一种基于多目标规划和支持向量机(SVM)的企业信用评估模型。基于TOPSIS法,分别以"正常企业"样本逼近理想点、"违约企业"样本逼近负理想点为目标,构建多目标规划模型;运用实码加速遗传算法求解得出指标综合权重,通过构造加权样本,减少两类样本企业信用状况的重叠,可在一定程度上提高SVM的预测精度。应用实例证明了该模型的可行性和有效性。
On the overlap of the credit conditions of two types of samples, this paper proposes an evaluation model for credit risk of enterprise based on multi -objective programming and Support Vector Machines （SVM）. Based on TOPSIS method, respectively taking the ＂normal enterprise＂ sample similarity to the ideal point and the ＂default enterprise＂ sample similarity to negative ideal point as the goal, the multi - objective programming model is established. Using real coded accelerating genetic algorithm （RAGA） , the model above is solved, and then the combination weight of index is obtained. Through constructing the weighted sample, the overlap of the credit conditions of two types of samples is reduced. As a result, the predicting accuracy of SVM can be raised to a certain extent. Through a specific example, it is proved that the model proposed by this paper is feasible and effective.
出处
《中国软科学》
CSSCI
北大核心
2009年第4期185-190,共6页
China Soft Science
基金
国家自然科学基金资助项目(70671017)
贵州省教育厅高校人文社会科学研究项目(2008-12)
关键词
信用风险
信用风险评估
多目标规划
加权样本
支持向量机
credit risk
evaluation of credit risk
multi - objective programming
weighted sample
Support Vector Ma- chines
