摘要
针对属性权重完全未知且数据为多维时序的信用风险评价问题,提出基于多属性决策与模糊聚类相结合的混杂信用风险评价建模方法.该方法使用离差最大化方法和二次规划模型得出指标属性的综合权重,对受评样本在各时点上进行多属性决策得到决策评分,再通过决策评分矩阵进行模糊聚类,并对结果进行了有效性验证.最后通过实例验证,证明该方法具有可行性和有效性.
To solve the credit risk evaluation problem in which the attribute weights are completely unknown and the data are multidimensional time series,a method of credit risk evaluation modeling based on the multi-attribute decision making and fuzzy clustering is presented.This method uses deviation-based maximization method and quadratic programming model to determine comprehensive weights of index attribute.The evaluated samples on each time point are scored by multi-attribute decision making,then fuzzy clustering is performed through the decision-making score matrix,and the effectiveness of result is validated.Finally,examples prove that the method is feasible and effective.
出处
《信息与控制》
CSCD
北大核心
2011年第5期692-697,共6页
Information and Control
基金
国家自然科学基金资助项目(60774068)
国家973计划资助项目(2002CB312201)
关键词
属性权重
线性规划
多属性决策
模糊聚类
attribute weight
linear programming
multi-attribute decision making
fuzzy clustering
