The nature of statistical learning theory
The nature of statistical learning theory
Neural network credit scoring models
Computers and Operations Research - Neural networks in business
Learning the Kernel Matrix with Semidefinite Programming
The Journal of Machine Learning Research
Multiple kernel learning, conic duality, and the SMO algorithm
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Learning the Kernel Function via Regularization
The Journal of Machine Learning Research
Dynamics of modeling in data mining: interpretive approach to bankruptcy prediction
Journal of Management Information Systems - Special section: Data mining
Computational Statistics & Data Analysis
A study of Taiwan's issuer credit rating systems using support vector machines
Expert Systems with Applications: An International Journal
classifications of credit cardholder behavior by using multiple criteria non-linear programming
CASDMKM'04 Proceedings of the 2004 Chinese academy of sciences conference on Data Mining and Knowledge Management
A new fuzzy support vector machine to evaluate credit risk
IEEE Transactions on Fuzzy Systems
A Neural Approach for SME's Credit Risk Analysis in Turkey
MLDM '09 Proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition
Knowledge discovery using neural approach for SME's credit risk analysis problem in Turkey
Expert Systems with Applications: An International Journal
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Recent studies have revealed that emerging modern machine learning techniques are advantageous to statistical models for credit risk evaluation, such as SVM. In this study, we discuss the applications of the support vector machine with mixture of kernel to design a credit evaluation system, which can discriminate good creditors from bad ones. Differing from the standard SVM, the SVM-MK uses the 1-norm based object function and adopts the convex combinations of single feature basic kernels. Only a linear programming problem needs to be resolved and it greatly reduces the computational costs. More important, it is a transparent model and the optimal feature subset can be obtained automatically. A real life credit dataset from a US commercial bank is used to demonstrate the good performance of the SVM-MK.