Using Gaussian process based kernel classifiers for credit rating forecasting

摘要

The subprime mortgage crisis have triggered a significant economic decline over the world. Credit rating forecasting has been a critical issue in the global banking systems. The study trained a Gaussian process based multi-class classifier (GPC), a highly flexible probabilistic kernel machine, using variational Bayesian methods. GPC provides full predictive distributions and model selection simultaneously. During training process, the input features are automatically weighted by their relevances with respect to the output labels. Benefiting from the inherent feature scaling scheme, GPCs outperformed convectional multi-class classifiers and support vector machines (SVMs). In the second stage, conventional SVMs enhanced by feature selection and dimensionality reduction schemes were also compared with GPCs. Empirical results indicated that GPCs still performed the best.