| 引用本文: | 柯孔林.基于粗糙集与支持向量机的企业短期贷款违约判别[J].控制理论与应用,2009,26(12):1365~1370.[点击复制] |
| KE Kong-lin.Default prediction of short-term loan based on integration of rough sets and support-vector-machines[J].Control Theory and Technology,2009,26(12):1365~1370.[点击复制] |
|
| 基于粗糙集与支持向量机的企业短期贷款违约判别 |
| Default prediction of short-term loan based on integration of rough sets and support-vector-machines |
| 摘要点击 1691 全文点击 889 投稿时间：2008-06-26 修订日期：2009-03-12 |
| 查看全文 查看/发表评论 下载PDF阅读器 |
| DOI编号 10.7641/j.issn.1000-8152.2009.12.CCTA080665 |
| 2009,26(12):1365-1370 |
| 中文关键词 粗糙集 支持向量机 神经网络 违约判别 |
| 英文关键词 rough set support-vector-machines neural network default prediction |
| 基金项目 国家教育部人文社会科学研究项目(08JC790096); 浙江省高校人文社会科学浙江工商大学金融学重点研究基地项目. |
|
| 中文摘要 |
| 建立了粗糙集和支持向量机集成的企业贷款违约判别模型, 该模型首先利用自组织映射(SOM)神经网络对具有连续属性值的财务数据进行离散处理, 并应用遗传算法约简评价指标, 然后将约简得到的最小条件属性集及相应的原始数据送入支持向量机进行训练, 最后对企业短期贷款检验样本进行违约判别. 采用贷款企业数据库558家制造业样本企业和522家房地产业样本企业进行交叉验证的实证研究, 结果表明, 与BP神经网络、多元判别分析、Logistic等违约判别模型相比, 粗糙集和支持向量机集成的违约判别模型有更好的预测效果. |
| 英文摘要 |
| An integrated model of rough sets and support-vector-machines for the default prediction of short-term loan is proposed. The financial data is discretized by using self-organizing mapping neural network; and the evaluation indices
are reduced with no information loss through genetic algorithm. The reduced indices together with relevant data are used to train support-vector-machines and discriminate between healthy and default testing samples. 558 manufacturing industrys loan firms and 522 real estate industrys loan firms are selected as test samples, The prediction accuracy of the integrated
model combining rough sets and support-vector-machines is better than that of other methods such as BP neural network, multiple discriminant analysis and logistic regression. |