Machine Learning
A bias-variance-complexity trade-off framework for complex system modeling
ICCSA'06 Proceedings of the 6th international conference on Computational Science and Its Applications - Volume Part I
Credit risk assessment with a multistage neural network ensemble learning approach
Expert Systems with Applications: An International Journal
Soft computing techniques applied to finance
Applied Intelligence
An Evolutionary Programming Based SVM Ensemble Model for Corporate Failure Prediction
ICANNGA '07 Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part II
Credit Risk Assessment Model of Commercial Banks Based on Fuzzy Neural Network
ISNN '09 Proceedings of the 6th International Symposium on Neural Networks on Advances in Neural Networks
Support vector machine based multiagent ensemble learning for credit risk evaluation
Expert Systems with Applications: An International Journal
Probabilistic and discriminative group-wise feature selection methods for credit risk analysis
Expert Systems with Applications: An International Journal
| Hi-index | 0.00 |
In the field of credit risk analysis, the problem that we often encountered is to increase the model accuracy as possible using the limited data. In this study, we discuss the use of supervised neural networks as a metalearning technique to design a credit scoring system to solve this problem. First of all, a bagging sampling technique is used to generate different training sets to overcome data shortage problem. Based on the different training sets, the different neural network models with different initial conditions or training algorithms is then trained to formulate different credit scoring models, i.e., base models. Finally, a neural-network-based metamodel can be produced by learning from all base models so as to improve the reliability, i.e., predict defaults accurately. For illustration, a credit card application approval experiment is performed.