基于SVM的小微企业评级
Rating of Small and Micro Businesses Based on SVM
摘要:
随着经济不断转型,小微企业为我国经济发展注入了新的活力。为了增强小微企业的风险管理水平,从而提高其成活率,就必须进行有效的风险评价。对我国小微企业进行风险评级,可以运用支持向量机(SVM)方法,并依据建立的风险评价指标体系,通过对选取的样本数据进行SVM分类训练,评估我国部分小微企业风险等级水平。从而方便企业管理者根据风险水平,采取切实有效风险控制措施,促进小微企业良性发展。
Abstract:
With the transformation of the economy, small and micro businesses have injected new vitality into the economic development of our country. In order to enhance the risk management level of small and micro businesses and to improve their survival rate, effective risk assessment must be carried out. We can use the method of support vector machine (SVM) for the risk rating of small and micro businesses. Based on the established risk assessment index system, we can evaluate the risk level of some small and micro businesses through SVM classification training on selected sample data. Therefore, it is convenient for the enterprise managers to take effective risk control measures to promote the benign development of small and micro businesses according to the risk level.
参考文献
|
[1]
|
侯合银. 高新技术创业企业风险的系统分析: 辨识与规避[J]. 科技管理研究, 2008, 28(10): 132-135.
|
|
[2]
|
袁莉, 李宏男. 基于SVM的建筑企业信用评价研究[J]. 价值工程, 2009, 28(3): 141-144.
|
|
[3]
|
姚奕, 叶中行. 基于支持向量机的银行客户信用评估系统研究[J]. 系统仿真学报, 2004, 16(4): 783-786.
|
|
[4]
|
章兢, 张小刚. 数据挖掘算法及其工程应用[M]. 北京: 机械工业出版社, 2006.
|
|
[5]
|
Li, L.M., Wen, G.R. and Wang, S.C. (2008) Parameters Selection of Support Vector Regression Based on Genetic Algorithm. Computer Engineering and Applications, 44, 23-26.
|