دانلود مقاله ISI انگلیسی شماره 50630
ترجمه فارسی عنوان مقاله

مطالعه تجربی از ارزیابی الگوریتم طبقه بندی برای پیش بینی ریسک مالی

عنوان انگلیسی
An empirical study of classification algorithm evaluation for financial risk prediction ☆
کد مقاله سال انتشار تعداد صفحات مقاله انگلیسی
50630 2011 10 صفحه PDF
منبع

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

Journal : Applied Soft Computing, Volume 11, Issue 2, March 2011, Pages 2906–2915

ترجمه کلمات کلیدی
الگوریتم طبقه بندی - معیارهای چندگانه تصمیم گیری (MCDM) - پیش بینی ریسک مالی - تجزیه و تحلیل ریسک مالی غنی از دانش
کلمات کلیدی انگلیسی
Classification algorithm; Multiple criteria decision making (MCDM); Financial risk prediction; Knowledge-rich financial risk analysis
پیش نمایش مقاله
پیش نمایش مقاله  مطالعه تجربی از ارزیابی الگوریتم طبقه بندی برای پیش بینی ریسک مالی

چکیده انگلیسی

A wide range of classification methods have been used for the early detection of financial risks in recent years. How to select an adequate classifier (or set of classifiers) for a given dataset is an important task in financial risk prediction. Previous studies indicate that classifiers’ performances in financial risk prediction may vary using different performance measures and under different circumstances. The main goal of this paper is to develop a two-step approach to evaluate classification algorithms for financial risk prediction. It constructs a performance score to measure the performance of classification algorithms and introduces three multiple criteria decision making (MCDM) methods (i.e., TOPSIS, PROMETHEE, and VIKOR) to provide a final ranking of classifiers. An empirical study is designed to assess various classification algorithms over seven real-life credit risk and fraud risk datasets from six countries. The results show that linear logistic, Bayesian Network, and ensemble methods are ranked as the top-three classifiers by TOPSIS, PROMETHEE, and VIKOR. In addition, this work discusses the construction of a knowledge-rich financial risk management process to increase the usefulness of classification results in financial risk detection.