摘要
针对高风险项目样本数据十分缺乏的问题,提出一种基于距离评判和支持向量数据描述的项目风险混合智能预警模型。通过对各传统风险评价指标进行距离评判,并根据评判因子的大小选取敏感指标作为支持向量数据描述的输入,实现对不同风险状态的自动识别。高技术项目投资风险预警实例表明,该方法可以有效提取敏感特征指标,降低数据维数,提高单值分类方法在项目风险智能预警中的准确性和可靠性。
Aiming at the problem of the lack of high risk projects＇data,a hybrid intelligent model of project risk prediction based on distance evaluation and Support Vector Data Description（SVDD） is proposed.By estimating the capacity of each traditional indicator in evaluating project risk,the sensitive features can be selected and input into SVDD to identify different conditions of project risk automatically.The results of the demonstration show that the model is efficient to extract the sensitive features,reduce the dimension of the data,and improve the veracity and reliability of one-class classification in intelligent risk prediction significantly.
出处
《计算机工程与应用》
CSCD
北大核心
2010年第5期189-192,共4页
Computer Engineering and Applications
基金
国家自然科学基金No.50705001
天津市教委重点调研课题(No.Jwdy-091013)~~
关键词
距离评判
支持向量数据描述
单值分类
风险预警
distance evaluation
support vector data description
one-class classification
risk prediction
