Credit Risk Evaluation Using SVM-Based Classifier

被引：0
|
作者：
Danenas, Paulius [1 ]
Garsva, Gintautas [1 ]
机构：
[1] Vilnius Univ, Kaunas Fac Humanities, Dept Informat, LT-44280 Kaunas, Lithuania
来源：
BUSINESS INFORMATION SYSTEMS WORKSHOPS | 2010年 / 57卷
关键词：
Support Vector Machines; SVM; artificial intelligence; machine learning; credit risk; evaluation; bankruptcy; Altman; FINANCIAL RATIOS; MODEL; PREDICTION;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This article presents a method joining Support Vector Machines (SVM), genetic search and multivariate analysis for identification of bankrupt companies. This study proposed to join widely used Altman Z-Score with Support Vector Machines to create a classifier that might be used to evaluate and forecast possible bankrupt companies. A genetic search algorithm is employed for relevant attribute selection to reduce the dimensionality of data.
引用
收藏
页码：7 / 12
页数：6
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