Relevance vector machine based infinite decision agent ensemble learning for credit risk analysis

In this paper, a relevance vector machine based infinite decision agent ensemble learning (RVMIdeal) 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 RVMIdeal 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.

► Based on the properties of credit risk modeling and existing machine learning techniques, we propose our model RVMIdeal.
► RVMIdeal is a three-level ensemble learning system. In the first level, 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 Kernels are employed in RVM to simulate infinite subagents.
► RVMIdeal has a stable structure, good generalization performance, can overcome overfitting and predict distance to default in the same time.