بهبود مطالعات تجربی در مورد آنسامبل های طبقه بندی کننده برای پیش بینی ورشکستگی و امتیازدهی اعتباری
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|48255||2014||6 صفحه PDF||سفارش دهید||5504 کلمه|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Expert Systems with Applications, Volume 41, Issue 8, 15 June 2014, Pages 3825–3830
Previous studies about ensembles of classifiers for bankruptcy prediction and credit scoring have been presented. In these studies, different ensemble schemes for complex classifiers were applied, and the best results were obtained using the Random Subspace method. The Bagging scheme was one of the ensemble methods used in the comparison. However, it was not correctly used. It is very important to use this ensemble scheme on weak and unstable classifiers for producing diversity in the combination. In order to improve the comparison, Bagging scheme on several decision trees models is applied to bankruptcy prediction and credit scoring. Decision trees encourage diversity for the combination of classifiers. Finally, an experimental study shows that Bagging scheme on decision trees present the best results for bankruptcy prediction and credit scoring.