The nature of statistical learning theory
The nature of statistical learning theory
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Neural network credit scoring models
Computers and Operations Research - Neural networks in business
Credit risk assessment with a multistage neural network ensemble learning approach
Expert Systems with Applications: An International Journal
Financial distress prediction based on OR-CBR in the principle of k-nearest neighbors
Expert Systems with Applications: An International Journal
Financial distress early warning based on group decision making
Computers and Operations Research
Ranking-order case-based reasoning for financial distress prediction
Knowledge-Based Systems
Gaussian case-based reasoning for business failure prediction with empirical data in China
Information Sciences: an International Journal
Credit scoring using support vector machines with direct search for parameters selection
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special issue on Uncertainty Analysis and Decision Making; Guest Editors: Yan-Kui Liu, Baoding Liu, Jinwu Gao
Predicting business failure using multiple case-based reasoning combined with support vector machine
Expert Systems with Applications: An International Journal
Business failure prediction using hybrid2 case-based reasoning (H2CBR)
Computers and Operations Research
Least squares support vector machines ensemble models for credit scoring
Expert Systems with Applications: An International Journal
Support vector machine based multiagent ensemble learning for credit risk evaluation
Expert Systems with Applications: An International Journal
Building credit scoring models using genetic programming
Expert Systems with Applications: An International Journal
A new fuzzy support vector machine to evaluate credit risk
IEEE Transactions on Fuzzy Systems
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Assessing scorecard performance: A literature review and classification
Expert Systems with Applications: An International Journal
A loan default discrimination model using cost-sensitive support vector machine improved by PSO
Information Technology and Management
| Hi-index | 12.06 |
Support vector machines (SVM) is proved to be one of the most effective tool in credit risk evaluation. However, the performance of SVM is sensitive not only to the algorithm for solving the quadratic programming but also to the parameters setting in its learning machines as well as to the importance of different classes. In order to solve these issues, this paper proposes a weighted least squares support vector machine (LSSVM) classifier with design of experiment (DOE) for parameter selection for credit risk evaluation. In this approach, least squares algorithm is used to solve the quadratic programming, the DOE is used for parameter selection in SVM modelling and weights in LSSVM are used to emphasize the importance of difference classes. For illustration purpose, two publicly available credit datasets are selected to demonstrate the effectiveness and feasibility of the proposed weighted LSSVM classifier. The results show that the proposed weighted LSSVM classifier with DOE can produce the promising classification results in credit risk evaluation, relative to other classifiers listed in this study.