Data mining feature selection for credit scoring models

Journal of the Operational Research Society

Volumn 56, Issue 9, 2005, Pages 1099-1108

  Liu, Y ^a,b   Schumann, M ^a  

^a UNIVERSITY OF GÖTTINGEN   (Germany)

^b Centre for Quantitative Economics Research of Jilin University   (China)

Author keywords

Classification algorithms; Credit scoring; Feature selection; Insurance; Risk

Indexed keywords

ALGORITHMS; BACKPROPAGATION; DATA MINING; INSURANCE; LEARNING SYSTEMS; MATHEMATICAL MODELS; MULTILAYER NEURAL NETWORKS; RISKS;

CLASSIFICATION ALGORITHM; CREDIT SCORING; CREDIT SCORING MODELS; FEATURE SELECTION;

FINANCE;

EID: 24144432307     PISSN: 01605682     EISSN: None     Source Type: Journal    
DOI: 10.1057/palgrave.jors.2601976     Document Type: Article

References (13)

1. Hand DJ and Henley WE (1997). Statistical classification methods in consumer credit scoring: a review. J R Stat Soc Seri A 160: 523-541.
2. Quinlan JR (1992). Learning with continuous classes. In: Adams A and Sterling L (eds). Proceedings of the 5th Australian Joint Conference on Artificial Intelligence. World Scientific, Singapore, pp 343-348.
3. Aha D, Kibler D and Albert M (1991). Instance-based learning algorithms. Mach Learn 6: 37-66.
4. Cios KJ, Pedrycz W and Swiniarski RW (1998). Data Mining Methods for Knowledge Discovery. Kluwer Academic Publishers: Boston.
5. Liu H and Motoda H (1998). Feature Selection for Knowledge Discovery and Data Mining. Kluwer Academic Publishers: London.
6. Kohavi R and John GH (1997). Wrappers for feature subset selection. Artif Intell 97: 273-324.
7. Hall MA and Holmes G (2000). Benchmarking attribute selection techniques for data mining. Working paper 00/10, Department of Computer Science, University of Waikato, Hamilton, New Zealand.
8. Kira K and Rendell LA (1992). A practical approach to feature selection. In: Sleeman D and Edwards P (eds). Proceedings of the 9th International Conference on Machine Learning. Morgan Kaufmann, Aberdeen, Scotland, pp 249-256.
9. Kononenko I (1994). Estimating attributes: analysis and extensions of RELIEF. In: De Raedt L and Bergadano F (eds). Proceedings of the European Conference on Machine Learning. Springer, Berlin, pp 171-182.
10. Hall MA (2000). Correlation-based feature selection for discrete and numeric class machine learning. In: Langley P (ed). Proceedings of the 17th International Conference on Machine Learning. Morgan Kaufmann, San Francisco, CA, pp 359-366.
11. Dash M, Liu H and Motoda H (2000). Consistency based feature selection. In: Terano T, Liu H and Chen ALP (eds). Knowledge Discovery and Data Mining: Current Issues and New Applications, the 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining. Springer, Berlin, pp 98-109.
12. Hall MA and Smith LA (1997). Feature subset selection: a correlation based filter approach. In: Kasabov N et al (eds). Proceedings of the 4th International Conference on Neural Information Processing and Intelligent Information Systems. Springer, Singapore, pp 855-858.
13. Witten IH and Frank E (2000). Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgen Kaufmann Publishers: San Francisco, CA.