Orthogonal support vector machine for credit scoring

被引:40
作者
Han, Lu [1 ,2 ]
Han, Liyan [1 ]
Zhao, Hongwei [2 ]
机构
[1] Beihang Univ, Sch Econ & Management, Beijing 100191, Peoples R China
[2] Tsinghua Univ, PBC Sch Finance, Beijing 100083, Peoples R China
基金
中国国家自然科学基金;
关键词
Dimension curse; Orthogonal dimension reduction; Support vector machine; Logistic regression; Principal component analysis; Credit scoring; ARTIFICIAL NEURAL-NETWORKS; DIMENSIONALITY REDUCTION; BANKRUPTCY PREDICTION; LOGISTIC-REGRESSION; CLASSIFICATION; OPTIMIZATION;
D O I
10.1016/j.engappai.2012.10.005
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The most commonly used techniques for credit scoring is logistic regression, and more recent research has proposed that the support vector machine is a more effective method. However, both logistic regression and support vector machine suffers from curse of dimension. In this paper, we introduce a new way to address this problem which is defined as orthogonal dimension reduction. We discuss the related properties of this method in detail and test it against other common statistical approaches principal component analysis and hybridizing logistic regression to better solve and evaluate the data. With experiments on German data set, there is also an interesting phenomenon with respect to the use of support vector machine, which we define as 'Dimensional interference', and discuss in general. Based on the results of cross-validation, it can be found that through the use of logistic regression filtering the dummy variables and orthogonal extracting feature, the support vector machine not only reduces complexity and accelerates convergence, but also achieves better performance. Crown Copyright (C) 2012 Published by Elsevier Ltd. All rights reserved.
引用
收藏
页码:848 / 862
页数:15
相关论文
共 32 条
[21]   Practical scheme for fast detection and classification of rolling-element bearing faults using support vector machines [J].
Rojas, Alfonso ;
Nandi, Asoke K. .
MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2006, 20 (07) :1523-1536
[22]   Use of particle swarm optimization for machinery fault detection [J].
Samanta, B. ;
Nataraj, C. .
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2009, 22 (02) :308-316
[23]   Support vector machines for classifying and describing credit applicants: detecting typical and critical regions [J].
Schebesch, KB ;
Stecking, R .
JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY, 2005, 56 (09) :1082-1088
[24]  
Sugiyama M, 2007, J MACH LEARN RES, V8, P1027
[25]  
Thomas L. C., 2019, CREDIT SCORING ITS A
[26]  
Van Gestel T., 2002, Least Squares Support Vector Machines
[28]   Adaptive credit scoring with kernel learning methods [J].
Yang, Yingxu .
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH, 2007, 183 (03) :1521-1536
[29]  
Yu L., 2008, BIOINSPIRED CREDIT R
[30]  
Yu L, 2006, LECT NOTES COMPUT SC, V4234, P380