دانلود مقاله ISI انگلیسی شماره 48580
ترجمه فارسی عنوان مقاله

پیش بینی کننده های پلی کیسه برای مدل سازی طبقه بندی برای امتیاز دادن اعتبار

عنوان انگلیسی
Poly-bagging predictors for classification modelling for credit scoring
کد مقاله سال انتشار تعداد صفحات مقاله انگلیسی ترجمه فارسی
48580 2011 4 صفحه PDF سفارش دهید
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منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Expert Systems with Applications, Volume 38, Issue 10, 15 September 2011, Pages 12717–12720

ترجمه کلمات کلیدی
نمره اعتباری، بسته بندی درخت طبقه بندی، ترکیب پیش بینی کننده، رگرسیون لجستیک
کلمات کلیدی انگلیسی
Credit scoring; Bagging; Classification tree; Combining predictors; Logistic regression

چکیده انگلیسی

Credit scoring modelling comprises one of the leading formal tools for supporting the granting of credit. Its core objective consists of the generation of a score by means of which potential clients can be listed in the order of the probability of default. A critical factor is whether a credit scoring model is accurate enough in order to provide correct classification of the client as a good or bad payer. In this context the concept of bootstraping aggregating (bagging) arises. The basic idea is to generate multiple classifiers by obtaining the predicted values from the fitted models to several replicated datasets and then combining them into a single predictive classification in order to improve the classification accuracy. In this paper we propose a new bagging-type variant procedure, which we call poly-bagging, consisting of combining predictors over a succession of resamplings. The study is derived by credit scoring modelling. The proposed poly-bagging procedure was applied to some different artificial datasets and to a real granting of credit dataset up to three successions of resamplings. We observed better classification accuracy for the two-bagged and the three-bagged models for all considered setups. These results lead to a strong indication that the poly-bagging approach may promote improvement on the modelling performance measures, while keeping a flexible and straightforward bagging-type structure easy to implement.

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