A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Computational Economics - Computational Studies at Stanford
Machine Learning
Sparse bayesian learning and the relevance vector machine
The Journal of Machine Learning Research
Credit rating analysis with support vector machines and neural networks: a market comparative study
Decision Support Systems - Special issue: Data mining for financial decision making
Credit scoring with a data mining approach based on support vector machines
Expert Systems with Applications: An International Journal
Credit risk assessment with a multistage neural network ensemble learning approach
Expert Systems with Applications: An International Journal
Support Vector Machinery for Infinite Ensemble Learning
The Journal of Machine Learning Research
A new two-stage hybrid approach of credit risk in banking industry
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
A study of Taiwan's issuer credit rating systems using support vector machines
Expert Systems with Applications: An International Journal
Variational relevance vector machines
UAI'00 Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence
Infinite ensemble learning with support vector machines
ECML'05 Proceedings of the 16th European conference on Machine Learning
Expert Systems with Applications: An International Journal
Credit rating using a hybrid voting ensemble
SETN'12 Proceedings of the 7th Hellenic conference on Artificial Intelligence: theories and applications
Expert Systems with Applications: An International Journal
International Journal of Mobile Learning and Organisation
Hi-index | 12.05 |
In this paper, a relevance vector machine based infinite decision agent ensemble learning (RVM"I"d"e"a"l) system is proposed for the robust credit risk analysis. In the first level of our model, we adopt soft margin boosting to overcome overfitting. In the second level, the RVM algorithm is revised for boosting so that different RVM agents can be generated from the updated instance space of the data. In the third level, the perceptron Kernel is employed in RVM to simulate infinite subagents. Our system RVM"I"d"e"a"l also shares some good properties, such as good generalization performance, immunity to overfitting and predicting the distance to default. According to the experimental results, our proposed system can achieve better performance in term of sensitivity, specificity and overall accuracy.