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Volumn 33, Issue 4, 2007, Pages 847-856

Credit scoring with a data mining approach based on support vector machines

Author keywords

Classification; Credit scoring; Data mining; Decision tree; Genetic programming; Neural networks; Support vector machine

Indexed keywords

DATA MINING; DECISION TREES; GENETIC PROGRAMMING; NEURAL NETWORKS; PATTERN RECOGNITION; PROBLEM SOLVING; SUPPORT VECTOR MACHINES;

EID: 34047126555     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2006.07.007     Document Type: Article
Times cited : (678)

References (47)
  • 2
    • 0012579787 scopus 로고    scopus 로고
    • The importance of credit scoring models in improving cash flow and collection
    • Brill J. The importance of credit scoring models in improving cash flow and collection. Business Credit 100 1 (1998) 16-17
    • (1998) Business Credit , vol.100 , Issue.1 , pp. 16-17
    • Brill, J.1
  • 3
    • 34047108293 scopus 로고    scopus 로고
    • Chang, C. C., & Lin, C. J. (2001) LIBSVM: a library for support vector machines. Available from http://www.csie.ntu.edu.tw/~cjlin/libsvm.
  • 4
    • 0037401347 scopus 로고    scopus 로고
    • Credit scoring and rejected instances reassigning through evolutionary computation techniques
    • Chen M.C., and Huang S.H. Credit scoring and rejected instances reassigning through evolutionary computation techniques. Expert Systems with Applications 24 4 (2003) 433-441
    • (2003) Expert Systems with Applications , vol.24 , Issue.4 , pp. 433-441
    • Chen, M.C.1    Huang, S.H.2
  • 5
    • 10644295762 scopus 로고    scopus 로고
    • The contribution of data mining to information science
    • Chen S.Y., and Liu X. The contribution of data mining to information science. Journal of Information Science 30 6 (2004) 550-558
    • (2004) Journal of Information Science , vol.30 , Issue.6 , pp. 550-558
    • Chen, S.Y.1    Liu, X.2
  • 6
    • 34047110873 scopus 로고    scopus 로고
    • Chen, Y.-W., & Lin, C.-J. (2005). Combining SVMs with various feature selection strategies. Available from http://www.csie.ntu.edu.tw/~cjlin/papers/features.pdf.
  • 9
    • 0030291564 scopus 로고    scopus 로고
    • A comparison of neural networks and linear scoring models in the credit union environment
    • Desai V.S., Crook J.N., and Overstreet G.A. A comparison of neural networks and linear scoring models in the credit union environment. European Journal of Operational Research 95 1 (1996) 24-37
    • (1996) European Journal of Operational Research , vol.95 , Issue.1 , pp. 24-37
    • Desai, V.S.1    Crook, J.N.2    Overstreet, G.A.3
  • 10
    • 0344235442 scopus 로고    scopus 로고
    • Fröhlich, H., & Chapelle, O. (2003). Feature selection for support vector machines by means of genetic algorithms. In Proceedings of the 15th IEEE international conference on tools with artificial intelligence, Sacramento, California, USA, pp. 142-148.
  • 11
    • 0001867238 scopus 로고
    • Interpreting neural-network connection weights
    • Garson G.D. Interpreting neural-network connection weights. AI Expert 6 4 (1991) 47-51
    • (1991) AI Expert , vol.6 , Issue.4 , pp. 47-51
    • Garson, G.D.1
  • 13
    • 0036161259 scopus 로고    scopus 로고
    • Gene selection for cancer classification using support vector machines
    • Guyon I., Weston J., Barnhill S., and Bapnik V. Gene selection for cancer classification using support vector machines. Machine Learning 46 1-3 (2002) 389-422
    • (2002) Machine Learning , vol.46 , Issue.1-3 , pp. 389-422
    • Guyon, I.1    Weston, J.2    Barnhill, S.3    Bapnik, V.4
  • 14
    • 34047137296 scopus 로고    scopus 로고
    • Henley, W. E. (1995). Statistical aspects of credit scoring. Dissertation, The Open University, Milton Keynes, UK.
  • 15
    • 1542603058 scopus 로고    scopus 로고
    • A k-nearest neighbor classifier for assessing consumer credit risk
    • Henley W.E., and Hand D.J. A k-nearest neighbor classifier for assessing consumer credit risk. Statistician 44 1 (1996) 77-95
    • (1996) Statistician , vol.44 , Issue.1 , pp. 77-95
    • Henley, W.E.1    Hand, D.J.2
  • 18
    • 17844388095 scopus 로고    scopus 로고
    • Hybrid mining approach in the design of credit scoring models
    • Hsieh N.-C. Hybrid mining approach in the design of credit scoring models. Expert Systems with Applications 28 4 (2005) 655-665
    • (2005) Expert Systems with Applications , vol.28 , Issue.4 , pp. 655-665
    • Hsieh, N.-C.1
  • 19
    • 34047162537 scopus 로고    scopus 로고
    • Hsu, C. W., Chang, C. C., & Lin, C. J. (2003). A practical guide to support vector classification. Available from http://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf.
  • 20
    • 0036158552 scopus 로고    scopus 로고
    • A simple decomposition method for support vector machine
    • Hsu C.W., and Lin C.J. A simple decomposition method for support vector machine. Machine Learning 46 1-3 (2002) 219-314
    • (2002) Machine Learning , vol.46 , Issue.1-3 , pp. 219-314
    • Hsu, C.W.1    Lin, C.J.2
  • 21
    • 2442665617 scopus 로고    scopus 로고
    • Credit rating analysis with support vector machines and neural networks: a market comparative study
    • Huang Z., Chen H., Hsu C.-J., Chen W.-H., and Wu S. Credit rating analysis with support vector machines and neural networks: a market comparative study. Decision Support Systems 37 4 (2004) 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
  • 22
    • 84957069814 scopus 로고    scopus 로고
    • Joachims, T. (1998). Text categorization with support vector machines. In Proceedings of European conference on machine learning (ECML), Chemintz, DE, pp.137-142.
  • 23
    • 34047192044 scopus 로고    scopus 로고
    • John, G., Kohavi, R., & Peger, K. (1994). Irrelevant features and the subset selection problem. In Proceedings of the eleventh international conference on machine learning, San Mateo, CA, USA, pp. 121-129.
  • 25
    • 0031381525 scopus 로고    scopus 로고
    • Wrappers for feature subset selection
    • Kohavi R., and John G. Wrappers for feature subset selection. Artificial Intelligence 97 1-2 (1997) 273-324
    • (1997) Artificial Intelligence , vol.97 , Issue.1-2 , pp. 273-324
    • Kohavi, R.1    John, G.2
  • 27
    • 17844382437 scopus 로고    scopus 로고
    • A two-stage hybrid credit scoring model using artificial neural networks and multivariate adaptive regression splines
    • Lee T.-S., and Chen I.-F. A two-stage hybrid credit scoring model using artificial neural networks and multivariate adaptive regression splines. Expert Systems with Applications 28 4 (2005) 743-752
    • (2005) Expert Systems with Applications , vol.28 , Issue.4 , pp. 743-752
    • Lee, T.-S.1    Chen, I.-F.2
  • 30
    • 0036027367 scopus 로고    scopus 로고
    • Differentiating between good credits and bad credits using neuro-fuzzy systems
    • Malhotra R., and Malhotra D.K. Differentiating between good credits and bad credits using neuro-fuzzy systems. European Journal of Operational Research 136 1 (2002) 190-211
    • (2002) European Journal of Operational Research , vol.136 , Issue.1 , pp. 190-211
    • Malhotra, R.1    Malhotra, D.K.2
  • 31
    • 0742307309 scopus 로고    scopus 로고
    • Feature subset selection for support vector machines through discriminative function pruning analysis
    • Mao K.Z. Feature subset selection for support vector machines through discriminative function pruning analysis. IEEE Transactions on Systems, Man, and Cybernetics 34 1 (2004) 60-67
    • (2004) IEEE Transactions on Systems, Man, and Cybernetics , vol.34 , Issue.1 , pp. 60-67
    • Mao, K.Z.1
  • 32
    • 34047180701 scopus 로고    scopus 로고
    • Murphy, P. M., Aha, D. W. (2001). UCI repository of machine learning databases. Department of Information and Computer Science, University of California Irvine, CA. Available from http://www.ics.uci.edu/mlearn/MLRepository.html.
  • 35
    • 33744584654 scopus 로고
    • Induction of decision trees
    • Quinlan J.R. Induction of decision trees. Machine Learning 1 1 (1986) 81-106
    • (1986) Machine Learning , vol.1 , Issue.1 , pp. 81-106
    • Quinlan, J.R.1
  • 37
    • 15944379834 scopus 로고
    • An examination of the conceptual issues involved in developing credit-scoring models
    • Reichert A.K., Cho C.C., and Wagner G.M. An examination of the conceptual issues involved in developing credit-scoring models. Journal of Business and Economic Statistics 1 2 (1983) 101-114
    • (1983) Journal of Business and Economic Statistics , vol.1 , Issue.2 , pp. 101-114
    • Reichert, A.K.1    Cho, C.C.2    Wagner, G.M.3
  • 38
    • 27144463192 scopus 로고    scopus 로고
    • On comparing classifiers: pitfalls to avoid and a recommended approach
    • Salzberg S.L. On comparing classifiers: pitfalls to avoid and a recommended approach. Data Mining and Knowledge Discovery 1 (1997) 317-327
    • (1997) Data Mining and Knowledge Discovery , vol.1 , pp. 317-327
    • Salzberg, S.L.1
  • 41
    • 84997479670 scopus 로고
    • Managerial applications of the neural networks: the case of bank failure prediction
    • Tam K.Y., and Kiang M.Y. Managerial applications of the neural networks: the case of bank failure prediction. Management Science 38 7 (1992) 926-947
    • (1992) Management Science , vol.38 , Issue.7 , pp. 926-947
    • Tam, K.Y.1    Kiang, M.Y.2
  • 42
    • 0001466281 scopus 로고    scopus 로고
    • A survey of credit and behavioural scoring: forecasting financial risk of lending to consumers
    • Thomas L.C. A survey of credit and behavioural scoring: forecasting financial risk of lending to consumers. International Journal of Forecasting 16 2 (2000) 149-172
    • (2000) International Journal of Forecasting , vol.16 , Issue.2 , pp. 149-172
    • Thomas, L.C.1
  • 44
    • 0034118581 scopus 로고    scopus 로고
    • Neural network credit scoring models
    • West D. Neural network credit scoring models. Computers and Operations Research 27 11-12 (2000) 1131-1152
    • (2000) Computers and Operations Research , vol.27 , Issue.11-12 , pp. 1131-1152
    • West, D.1
  • 46
    • 84960349226 scopus 로고    scopus 로고
    • Yu, G. X., Ostrouchov, G., Geist, A., & Samatova, N. F. (2003). An SVM-based algorithm for identification of photosynthesis-specific genome features. In 2nd IEEE computer society bioinformatics conference, CA, USA, pp. 235-243.


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.