دانلود مقاله ISI انگلیسی شماره 48612
عنوان فارسی مقاله

استفاده از آنسامبل های شبکه های عصبی برای پیش بینی ورشکستگی و امتیازدهی اعتباری

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
48612 2008 11 صفحه PDF سفارش دهید محاسبه نشده
خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.
عنوان انگلیسی
Using neural network ensembles for bankruptcy prediction and credit scoring
منبع

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

Journal : Expert Systems with Applications, Volume 34, Issue 4, May 2008, Pages 2639–2649

کلمات کلیدی
پیش بینی ورشکستگی - اعتبارسنجی - شبکه های عصبی - آنسامبل های طبقه بندی کننده
پیش نمایش مقاله
پیش نمایش مقاله استفاده از آنسامبل های شبکه های عصبی برای پیش بینی ورشکستگی و امتیازدهی اعتباری

چکیده انگلیسی

Bankruptcy prediction and credit scoring have long been regarded as critical topics and have been studied extensively in the accounting and finance literature. Artificial intelligence and machine learning techniques have been used to solve these financial decision-making problems. The multilayer perceptron (MLP) network trained by the back-propagation learning algorithm is the mostly used technique for financial decision-making problems. In addition, it is usually superior to other traditional statistical models. Recent studies suggest combining multiple classifiers (or classifier ensembles) should be better than single classifiers. However, the performance of multiple classifiers in bankruptcy prediction and credit scoring is not fully understood. In this paper, we investigate the performance of a single classifier as the baseline classifier to compare with multiple classifiers and diversified multiple classifiers by using neural networks based on three datasets. By comparing with the single classifier as the benchmark in terms of average prediction accuracy, the multiple classifiers only perform better in one of the three datasets. The diversified multiple classifiers trained by not only different classifier parameters but also different sets of training data perform worse in all datasets. However, for the Type I and Type II errors, there is no exact winner. We suggest that it is better to consider these three classifier architectures to make the optimal financial decision.

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