Support vector machines for credit scoring and discovery of significant features

被引：167

作者：

Bellotti, Tony ^{[1
]}

Crook, Jonathan ^{[1
]}

机构：

[1] Univ Edinburgh, Management Sch & Econ, Credit Res Ctr, Edinburgh EH8 9JY, Midlothian, Scotland

来源：

EXPERT SYSTEMS WITH APPLICATIONS | 2009年 / 36卷 / 02期

基金：

英国工程与自然科学研究理事会;

关键词：

SVM; Credit scoring; Feature selection; CLASSIFIER TECHNOLOGY; ILLUSION; PROGRESS;

D O I：

10.1016/j.eswa.2008.01.005

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The assessment of risk of default oil credit is important for financial institutions. Logistic regression and discriminant analysis are techniques traditionally used in credit scoring for determining likelihood to default based on consumer application and credit reference agency data. We test support vector machines against these traditional methods on a large credit card database. We find that they are competitive and can be used as the basis of a feature selection method to discover those features that are most significant in determining risk of default. (C) 2008 Elsevier Ltd. All rights reserved.

引用

页码：3302 / 3308

页数：7

共 50 条

[41] Multi-Class Support Vector Machine for Credit Scoring
Tang, Bo
Qiu, Saibing
[J]. ADVANCES IN COMPUTING, CONTROL AND INDUSTRIAL ENGINEERING, 2012, 235 : 419 - 422
[42] Deep Features for Training Support Vector Machines
Nanni, Loris
Ghidoni, Stefano
Brahnam, Sheryl
[J]. JOURNAL OF IMAGING, 2021, 7 (09)
[43] Support vector machines applied to protein ligand scoring.
Goldman, BB
[J]. ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY, 2001, 222 : U388 - U388
[44] SUPPORT VECTOR MACHINES AND THEIR APPLICATION IN CREDIT RISK EVALUATION PROCESS
Danenas, Paulius
Garsva, Gintautas
[J]. TRANSFORMATIONS IN BUSINESS & ECONOMICS, 2009, 8 (03): : 46 - 58
[45] Application of support vector machines to corporate credit rating prediction
Lee, Young-Chan
[J]. EXPERT SYSTEMS WITH APPLICATIONS, 2007, 33 (01) : 67 - 74
[46] Support vector machines for credit risk assessment with imbalanced datasets
Khemakhem, Sihem
Boujelbene, Younes
[J]. INTERNATIONAL JOURNAL OF DATA MINING MODELLING AND MANAGEMENT, 2018, 10 (02) : 171 - 187
[47] Advances with support vector machines for novel drug discovery
Maltarollo, Vinicius Goncalves
Kronenberger, Thales
Espinoza, Gabriel Zarzana
Oliveira, Patricia Rufino
Honorio, Kathia Maria
[J]. EXPERT OPINION ON DRUG DISCOVERY, 2019, 14 (01) : 23 - 33
[48] Combining market and accounting-based models for credit scoring using a classification scheme based on support vector machines
Niklis, Dimitrios
Doumpos, Michael
Zopounidis, Constantin
[J]. APPLIED MATHEMATICS AND COMPUTATION, 2014, 234 : 69 - 81
[49] Credit Scoring with F-score Based on Support Vector Machine
Chen, Weisong
Shi, Liang
[J]. PROCEEDINGS 2013 INTERNATIONAL CONFERENCE ON MECHATRONIC SCIENCES, ELECTRIC ENGINEERING AND COMPUTER (MEC), 2013, : 1512 - 1516
[50] EXPECTED KERNEL FOR MISSING FEATURES IN SUPPORT VECTOR MACHINES
Anderson, Hyrum S.
Gupta, Maya R.
[J]. 2011 IEEE STATISTICAL SIGNAL PROCESSING WORKSHOP (SSP), 2011, : 285 - 288

← 1 2 3 4 5 →