Using multiple SVM models for unbalanced credit scoring data sets

Studies in Classification, Data Analysis, and Knowledge Organization

Volumn , Issue , 2008, Pages 515-522

  Schebesch, Klaus B ^a   Stecking, Ralf ^b  

^a Vasile Goldi Western University   (Romania)

^b UNIVERSITY OF OLDENBURG   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION (OF INFORMATION); DATA HANDLING; MACHINE LEARNING;

CLASSIFICATION ACCURACY; CLASSIFICATION METHODS; CLASSIFICATION PERFORMANCE; CREDIT SCORING MODEL; MACHINE LEARNING COMMUNITIES; MISCLASSIFICATION COSTS; MULTIPLE KERNELS; UNBALANCED DATA;

SUPPORT VECTOR MACHINES;

EID: 84879561327     PISSN: 14318814     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1007/978-3-540-78246-9_61     Document Type: Conference Paper

References (9)

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6. SCHEBESCH, K.B. and STECKING, R. (2005a): Support Vector Machines for Credit Scoring: Extension to Non Standard Cases. In: Baier, D. andWernecke, K.-D. (Eds.): Innovations in Classification, Data Science and Information Systems. Springer, Berlin, 498-505.
7. SCHEBESCH, K.B. and STECKING, R. (2005b): Support vector machines for credit applicants: detecting typical and critical regions. Journal of the Operational Research Society, 56(9), 1082-1088.
8. SCHEBESCH, K.B. and STECKING, R. (2007): Selecting SVM Kernels and Input Variable Subsets in Credit Scoring Models. In: Decker, R. and Lenz, H.-J. (Eds.): Advances in Data Analysis. Springer, Berlin, 179-186.
9. STECKING, R. and SCHEBESCH, K.B. (2006): Comparing and Selecting SVM-Kernels for Credit Scoring. In: Spiliopoulou, M., Kruse, R., Borgelt, C., Nürnberger, A., Gaul, W. (Eds.): From Data and Information Analysis to Knowledge Engineering. Springer, Berlin, 542-549.