A Method of Improving Credit Evaluation with Support Vector Machines

被引:0
作者
Chen, Jingnian [1 ]
Xu, Li [2 ]
机构
[1] Shandong Univ Finance & Econ, Dept Informat & Comp Sci, Jinan 250014, Peoples R China
[2] Jinan Rd Management Bur, Jinan 250013, Peoples R China
来源
2015 11TH INTERNATIONAL CONFERENCE ON NATURAL COMPUTATION (ICNC) | 2015年
关键词
Classification; Support Vector Machines; Credit scoring; Whitening transformation; CARD FRAUD; SVM; PERFORMANCE; PREDICTION; ALGORITHM; RISK;
D O I
暂无
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
With the growth of credit business scale, credit evaluation models are becoming more and more popular for credit admission decision with minimum risk. Among many of these models, the support vector machine (SVM) draws more attention for its effectiveness. In this work, we presented a new skill to further improve the effect of SVM for credit scoring. In the learning process of SVM, we utilized the whitening transformation, which is usually adopted in signal processing. We applied our method on two real credit data sets, and found that not only the credit scoring accuracy can be enormously improved, but the learning time of SVM models can also be obviously reduced.
引用
收藏
页码:615 / 619
页数:5
相关论文
共 32 条
[1]   Genetic programming for credit scoring: The case of Egyptian public sector banks [J].
Abdou, Hussein A. .
EXPERT SYSTEMS WITH APPLICATIONS, 2009, 36 (09) :11402-11417
[2]   Financial Intelligence in Prediction of Firm's Creditworthiness Risk: Evidence from Support Vector Machine Approach [J].
Benhayoun, Nesrin ;
Chairi, Ikram ;
El Gonnouni, Amina ;
Lyhyaoui, Abdelouahid .
INTERNATIONAL CONFERENCE ON APPLIED ECONOMICS (ICOAE) 2013, 2013, 5 :103-112
[3]   Data mining for credit card fraud: A comparative study [J].
Bhattacharyya, Siddhartha ;
Jha, Sanjeev ;
Tharakunnel, Kurian ;
Westland, J. Christopher .
DECISION SUPPORT SYSTEMS, 2011, 50 (03) :602-613
[4]   A tutorial on Support Vector Machines for pattern recognition [J].
Burges, CJC .
DATA MINING AND KNOWLEDGE DISCOVERY, 1998, 2 (02) :121-167
[5]   LIBSVM: A Library for Support Vector Machines [J].
Chang, Chih-Chung ;
Lin, Chih-Jen .
ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY, 2011, 2 (03)
[6]   Choosing multiple parameters for support vector machines [J].
Chapelle, O ;
Vapnik, V ;
Bousquet, O ;
Mukherjee, S .
MACHINE LEARNING, 2002, 46 (1-3) :131-159
[7]   Combination of feature selection approaches with SVM in credit scoring [J].
Chen, Fei-Long ;
Li, Feng-Chia .
EXPERT SYSTEMS WITH APPLICATIONS, 2010, 37 (07) :4902-4909
[8]  
Cherkassky V, 1997, IEEE Trans Neural Netw, V8, P1564, DOI 10.1109/TNN.1997.641482
[9]  
Christianini N., 2000, An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods
[10]   Applications of support vector machines to cancer classification with microarray data [J].
Chu, F ;
Wang, LP .
INTERNATIONAL JOURNAL OF NEURAL SYSTEMS, 2005, 15 (06) :475-484