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دسته بندی:
مدیریت بازرگانی - Business Management
سال انتشار:
2020
عنوان انگلیسی مقاله:
Dynamic imbalanced business credit evaluation based on Learn++ with sliding time window and weight sampling and FCM with multiple kernels
ترجمه فارسی عنوان مقاله:
ارزیابی اعتبار نامتعادل پویای تجاری بر اساس یادگیری ++ با پنجره زمانی کشویی و نمونه برداری از وزن و FCM با چندین هسته
منبع:
Sciencedirect - Elsevier - Information Sciences, 520 (2020) 305-323: doi:10:1016/j:ins:2020:02:011
نویسنده:
Lu Wang
چکیده انگلیسی:
A good model of business credit evaluation is an important tool for risk management. Although the dynamic imbalanced data flow is more consistent with the form of collected
financial data in the actual situation, existing studies seldom research financial data as this
form. This paper proposes a new ensemble model for dynamic imbalanced business credit
evaluation based on the improved Learn++ and fuzzy c-means (FCM). To handle dynamic
imbalanced financial data, Learn++ is improved by using a sliding time window (STW)
and weight sampling (WS). This method is termed Learn++.STW-WS. STW can divide data
with the same concept into the same dataset to solve the problem of concept drift which
characteristic in dynamic data. Additionally, WS can redistribute the weights for samples of
different classes to resolve the issue of imbalance. To satisfy the demand of Learn++.STWWS on the prediction accuracy of a base classifier, FCM is improved by multiple kernels
(MK), and is designated as MK-FCM. Several kernel functions are integrated to construct
MK by the mean method, and MK is adopted to improve the calculation method of distances among points for FCM. Therefore, this new ensemble model can solve the problems
of dynamic data and imbalanced classes at the same time. In the empirical research, financial data from Chinese listed companies are selected to evaluate business credit risk,
and the associated models are adopted to make comparative analysis. The experiment results can fully demonstrate the good performance of the new ensemble model in terms of
handling dynamic imbalanced financial data.
Keywords: Business credit evaluation | Dynamic imbalanced financial data | Ensemble model | Learn++ | Fuzzy c-means
قیمت: رایگان
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