The nature of statistical learning theory
The nature of statistical learning theory
Least Squares Support Vector Machine Classifiers
Neural Processing Letters
Credit Scoring and Its Applications
Credit Scoring and Its Applications
Credit risk assessment with a multistage neural network ensemble learning approach
Expert Systems with Applications: An International Journal
An Intelligent CRM System for Identifying High-Risk Customers: An Ensemble Data Mining Approach
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part II
Support vector machine based multiagent ensemble learning for credit risk evaluation
Expert Systems with Applications: An International Journal
Evolving least squares support vector machines for stock market trend mining
IEEE Transactions on Evolutionary Computation - Special issue on computational finance and economics
Credit risk evaluation with kernel-based affine subspace nearest points learning method
Expert Systems with Applications: An International Journal
Fuzzy type 2 inference system for credit scoring
ACMOS'09 Proceedings of the 11th WSEAS international conference on Automatic control, modelling and simulation
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Credit risk evaluation has been the major focus of financial and banking industry due to recent financial crises and regulatory concern of Basel II. Recent studies have revealed that emerging artificial intelligent techniques are advantageous to statistical models for credit risk evaluation. In this study, we discuss the use of least square support vector machine (LSSVM) technique to design a credit risk evaluation system to discriminate good creditors from bad ones. Relative to the Vapnik's support vector machine, the LSSVM can transform a quadratic programming problem into a linear programming problem thus reducing the computational complexity. For illustration, a published credit dataset for consumer credit is used to validate the effectiveness of the LSSVM