A novel support vector machine metamodel for business risk identification

被引:0
|
作者
Lai, Kin Keung [1 ]
Yu, Lean
Huang, Wei
Wang, Shouyang
机构
[1] Hunan Univ, Coll Business Adm, Changsha 410082, Peoples R China
[2] City Univ Hong Kong, Dept Management Sci, Kowloon, Hong Kong, Peoples R China
[3] Huazhong Univ Sci & Technol, Sch Management, Wuhan 430074, Peoples R China
[4] Chinese Acad Sci, Inst Syst Sci, Acad Math & Syst Sci, Beijing 100080, Peoples R China
来源
PRICAI 2006: TRENDS IN ARTIFICIAL INTELLIGENCE, PROCEEDINGS | 2006年 / 4099卷
关键词
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In this study, support vector machine (SVM) is used as a metamodeling technique to design a business risk identification system. First of all, a bagging sampling technique is used to generate different training sets. Based on the different training sets, different SVM models with different parameters, i.e., base models, are then trained to formulate different classifiers. Finally, a SVM-based metamodel (i.e., metaclassifier) can be produced by learning from all base models. For illustration the proposed metamodel is applied to a real-world business insolvency risk classification problem.
引用
收藏
页码:980 / 984
页数:5
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