کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
10322586 660862 2012 13 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Feature selection using Bayesian and multiclass Support Vector Machines approaches: Application to bank risk prediction
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
پیش نمایش صفحه اول مقاله
Feature selection using Bayesian and multiclass Support Vector Machines approaches: Application to bank risk prediction
چکیده انگلیسی
This paper presents methods of banks discrimination according to the rate of NonPerforming Loans (NPLs), using Gaussian Bayes models and different approaches of multiclass Support Vector Machines (SVM). This classification problem involves many irrelevant variables and comparatively few training instances. New variable selection strategies are proposed. They are based on Gaussian marginal densities for Bayesian models and ranking scores derived from multiclass SVM. The results on both toy data and real-life problem of banks classification demonstrate a significant improvement of prediction performance using only a few variables. Moreover, Support Vector Machines approaches are shown to be superior to Gaussian Bayes models.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Expert Systems with Applications - Volume 39, Issue 3, 15 February 2012, Pages 3087-3099
نویسندگان
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