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Volumn 7, Issue , 2008, Pages 8-12

A hybrid credit scoring model based on genetic programming and support vector machines

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER PROGRAMMING; DECISION THEORY; DECISION TREES; GENETIC ALGORITHMS; IMAGE RETRIEVAL; MATHEMATICAL MODELS; NEURAL NETWORKS; SUPPORT VECTOR MACHINES; VECTORS;

EID: 57649205422     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICNC.2008.205     Document Type: Conference Paper
Times cited : (29)

References (27)
  • 1
    • 57649190691 scopus 로고    scopus 로고
    • Edelman, David B. Edelman, Jonathan N Crook
    • Philadelphia: SIAM
    • Thomas, Lyn C., Edelman, David B. Edelman, Jonathan N Crook. Credit Scoring and its Applications. Philadelphia: SIAM, 2002.
    • (2002) Credit Scoring and its Applications
    • Thomas, L.C.1
  • 4
    • 26444554549 scopus 로고    scopus 로고
    • Mining the Customer Credit Using Classification and Regression Tree and Multivariate Adaptive Regression Splines
    • Tian-Shyug Leea, Chih-Chou Chiub, Yu-Chao Chouc, Chi-Jie Lud. Mining the Customer Credit Using Classification and Regression Tree and Multivariate Adaptive Regression Splines. Computational Statistics and Data Analysis 2006; 50:1113-1130.
    • (2006) Computational Statistics and Data Analysis , vol.50 , pp. 1113-1130
    • Leea, T.-S.1    Chiub, C.-C.2    Chouc, Y.-C.3    Lud, C.-J.4
  • 5
    • 0034118581 scopus 로고    scopus 로고
    • Neural network credit scoring models
    • West D. Neural network credit scoring models. Computers & Operations Research 2000; 27:1131-52.
    • (2000) Computers & Operations Research , vol.27 , pp. 1131-1152
    • West, D.1
  • 6
    • 2442665617 scopus 로고    scopus 로고
    • Credit Rating Analysis with Support Vector Machines and Neural Network: A Market Comparative Study
    • Huang, Z Chen, H., Hsu, C.J., Chen, W.-H, Wu, S. Credit Rating Analysis with Support Vector Machines and Neural Network: A Market Comparative Study. Decision Support Systems 2004; 37(4):543-558.
    • (2004) Decision Support Systems , vol.37 , Issue.4 , pp. 543-558
    • Huang, Z.1    Chen, H.2    Hsu, C.J.3    Chen, W.-H.4    Wu, S.5
  • 7
    • 33746625785 scopus 로고    scopus 로고
    • Support Vector Machines Approach to Credit Assessment
    • International Conference on Computational Science
    • Jianping Li, Jingli Liu, Weixuan Xu, Yong Shi. Support Vector Machines Approach to Credit Assessment. International Conference on Computational Science 2004; LNCS 3039:892-899.
    • (2004) LNCS , vol.3039 , pp. 892-899
    • Li, J.1    Liu, J.2    Xu, W.3    Shi, Y.4
  • 8
    • 33749577626 scopus 로고    scopus 로고
    • Kin Keung Lai, Lean Yu, Shouyang Wang, Ligang Zhou: Neural Network Metalearning for Credit Scoring. ICIC (1)2006: 403-408.
    • Kin Keung Lai, Lean Yu, Shouyang Wang, Ligang Zhou: Neural Network Metalearning for Credit Scoring. ICIC (1)2006: 403-408.
  • 10
    • 38049179196 scopus 로고    scopus 로고
    • Building Behavior Scoring Model Using Genetic Algorithm and Support Vector Machines
    • De-Fu Zhang, Qing-Shan Chen, Li-jun Wei. Building Behavior Scoring Model Using Genetic Algorithm and Support Vector Machines. Lecture Notes in Computer Science. 2007,4488: 482-485.
    • (2007) Lecture Notes in Computer Science , vol.4488 , pp. 482-485
    • Zhang, D.-F.1    Chen, Q.-S.2    Wei, L.-J.3
  • 16
    • 0036079474 scopus 로고    scopus 로고
    • Discovering interesting classification rules with genetic programming
    • De Falco, A. Della Cioppa, E. Tarantino. Discovering interesting classification rules with genetic programming. Applied Soft Computing 2002; 1(3): 257-269.
    • (2002) Applied Soft Computing , vol.1 , Issue.3 , pp. 257-269
    • Falco, D.1    Della Cioppa, A.2    Tarantino, E.3
  • 17
    • 0041413815 scopus 로고
    • Commercial bank lending: Process, credit scoring, and costs of errors in lending
    • Airman E I. Commercial bank lending: process, credit scoring, and costs of errors in lending. Journal of Financial and Quantitative Analysis 1980; 15:813-832.
    • (1980) Journal of Financial and Quantitative Analysis , vol.15 , pp. 813-832
    • Airman, E.I.1
  • 20
    • 57649190705 scopus 로고    scopus 로고
    • Chang, C. C., and Lin, C. (J.). LIBSVM: a library for support vector machines. Available: http://www.csie.ntu.edu.tw/~cilin/libsvm.
    • Chang, C. C., and Lin, C. (J.). LIBSVM: a library for support vector machines. Available: http://www.csie.ntu.edu.tw/~cilin/libsvm.
  • 21
    • 2442665617 scopus 로고    scopus 로고
    • Credit rating analysis with support vector machine and neural networks: A market comparative study
    • Huang, Z., Chen, H., Hsu, C.-J., Chen, W.-H., Wu, (S.). Credit rating analysis with support vector machine and neural networks: A market comparative study. Decision Support Systems 2004; 37:543-558.
    • (2004) Decision Support Systems , vol.37 , pp. 543-558
    • Huang, Z.1    Chen, H.2    Hsu, C.-J.3    Chen, W.-H.4    Wu, S.5
  • 23
    • 57649156648 scopus 로고    scopus 로고
    • Quinlan, J. R. C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, 1993.
    • Quinlan, J. R. C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, 1993.
  • 24
    • 16244405300 scopus 로고    scopus 로고
    • Building credit scoring models using genetic programming
    • Chorng-Shyong Ong, Jih-Jeng Huang, Gwo-Hshiung Tzheng. Building credit scoring models using genetic programming. Expert Systems with Applications 2005; 19(1): 41-47.
    • (2005) Expert Systems with Applications , vol.19 , Issue.1 , pp. 41-47
    • Ong, C.-S.1    Huang, J.-J.2    Tzheng, G.-H.3
  • 26
    • 17844388095 scopus 로고    scopus 로고
    • Hybrid mining approach in the design of credit scoring models
    • Nan-Chen Hsieh. Hybrid mining approach in the design of credit scoring models. Expert Systems with Applications 2005; 28:655-665.
    • (2005) Expert Systems with Applications , vol.28 , pp. 655-665
    • Hsieh, N.-C.1
  • 27
    • 0031209593 scopus 로고    scopus 로고
    • Determining the saliency of input variables in neural network classifiers
    • R. Nath, B. Rajagopalan, R. Ryker. Determining the saliency of input variables in neural network classifiers. Computers & Operations Research 1997; 24(8): 767-73.
    • (1997) Computers & Operations Research , vol.24 , Issue.8 , pp. 767-773
    • Nath, R.1    Rajagopalan, B.2    Ryker, R.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.