دانلود مقاله ISI انگلیسی شماره 39891
عنوان فارسی مقاله

درخت های تصمیم گیری حساس به هزینه وابسته به مثال

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
39891 2015 11 صفحه PDF سفارش دهید 8280 کلمه
خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.
عنوان انگلیسی
Example-dependent cost-sensitive decision trees
منبع

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

Journal : Expert Systems with Applications, Volume 42, Issue 19, 1 November 2015, Pages 6609–6619

کلمات کلیدی
هزینه حساس یادگیری - طبقه بندی حساس به هزینه - اعتبارسنجی - تشخیص تقلب - بازاریابی مستقیم - درخت های تصمیم گیری
پیش نمایش مقاله
پیش نمایش مقاله درخت های تصمیم گیری حساس به هزینه وابسته به مثال

چکیده انگلیسی

Several real-world classification problems are example-dependent cost-sensitive in nature, where the costs due to misclassification vary between examples. However, standard classification methods do not take these costs into account, and assume a constant cost of misclassification errors. State-of-the-art example-dependent cost-sensitive techniques only introduce the cost to the algorithm, either before or after training, therefore, leaving opportunities to investigate the potential impact of algorithms that take into account the real financial example-dependent costs during an algorithm training. In this paper, we propose an example-dependent cost-sensitive decision tree algorithm, by incorporating the different example-dependent costs into a new cost-based impurity measure and a new cost-based pruning criteria. Then, using three different databases, from three real-world applications: credit card fraud detection, credit scoring and direct marketing, we evaluate the proposed method. The results show that the proposed algorithm is the best performing method for all databases. Furthermore, when compared against a standard decision tree, our method builds significantly smaller trees in only a fifth of the time, while having a superior performance measured by cost savings, leading to a method that not only has more business-oriented results, but also a method that creates simpler models that are easier to analyze.

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