Using Gaussian process based kernel classifiers for credit rating forecasting

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.

Research highlights
► We used a new Gaussian process based classifier (GPC) for credit scoring.
► GPC is a Bayesian probabilistic kernel machine.
► Predictive distributions and model selection are simultaneously provided.
► Inherent feature scaling of GPC improves classification performance.
► GPCs outperform convectional classifiers.