کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
385687 660869 2011 5 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Using Gaussian process based kernel classifiers for credit rating forecasting
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
پیش نمایش صفحه اول مقاله
Using Gaussian process based kernel classifiers for credit rating forecasting
چکیده انگلیسی

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.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Expert Systems with Applications - Volume 38, Issue 7, July 2011, Pages 8607–8611
نویسندگان
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