A two-phase model based on SVM and conjoint analysis for credit scoring

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 4488 LNCS, Issue PART 2, 2007, Pages 494-498

  Kin, Keung Lai ^a   Ligang, Zhou ^a   Lean, Yu ^a  

^a CITY UNIVERSITY OF HONG KONG   (Hong Kong)

Author keywords

Conjoint analysis; Credit scoring; Support vector machines

Indexed keywords

DATA STRUCTURES; DECISION MAKING; MATHEMATICAL MODELS;

CONJOINT ANALYSIS; CREDIT SCORING;

SUPPORT VECTOR MACHINES;

EID: 38049115988     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: None     Document Type: Conference Paper

References (5)

1. Thomas, L.C.: A Survey of Credit and Behavioural Scoring: Forecasting Financial Risk of Lending to Consumers. International Journal of Forecasting 16 (2000) 149-172
2. Suykens, J.A.K., Gestel, T.V., Brabanter, J.D., Moor, B.D., Vandewalle, J.: Least Squares Support Vector Machines. World Scientific, Siningapore (2002)
3. STAELIN, C.: Parameter Selection for Support Vector Machines. Tech. Rep., HPL-2002-354 (R. 1), HP Laboratories Israel, (2003)
4. Huang, C.L., Chen, M.C., Wang, C.J.: Credit Scoring with a Data Mining Approach Based on Support Vector Machines. Expert Systems with Applications (2006) doi:10.1016/j.eswa.2006.1007.1007
5. West, D.: Neural Network Credit Scoring Models. Computers & Operations Research 27 (2000) 1131-1152