کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4943470 | 1437633 | 2017 | 10 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
A comparative study on base classifiers in ensemble methods for credit scoring
ترجمه فارسی عنوان
یک مطالعه تطبیقی بر طبقه بندی های پایه در روش های گروهی برای برآورد اعتبار
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
چکیده انگلیسی
In the last years, the application of artificial intelligence methods on credit risk assessment has meant an improvement over classic methods. Small improvements in the systems about credit scoring and bankruptcy prediction can suppose great profits. Then, any improvement represents a high interest to banks and financial institutions. Recent works show that ensembles of classifiers achieve the better results for this kind of tasks. In this paper, it is extended a previous work about the selection of the best base classifier used in ensembles on credit data sets. It is shown that a very simple base classifier, based on imprecise probabilities and uncertainty measures, attains a better trade-off among some aspects of interest for this type of studies such as accuracy and area under ROC curve (AUC). The AUC measure can be considered as a more appropriate measure in this grounds, where the different type of errors have different costs or consequences. The results shown here present to this simple classifier as an interesting choice to be used as base classifier in ensembles for credit scoring and bankruptcy prediction, proving that not only the individual performance of a classifier is the key point to be selected for an ensemble scheme.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Expert Systems with Applications - Volume 73, 1 May 2017, Pages 1-10
Journal: Expert Systems with Applications - Volume 73, 1 May 2017, Pages 1-10
نویسندگان
JoaquÃn Abellán, Javier G. Castellano,