کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
1148556 | 957840 | 2007 | 13 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Assessing the stability of classification trees using Florida birth data
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
ریاضیات
ریاضیات کاربردی
پیش نمایش صفحه اول مقاله
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چکیده انگلیسی
Using 1998 and 1999 singleton birth data of the State of Florida, we study the stability of classification trees. Tree stability depends on both the learning algorithm and the specific data set. In this study, test samples are used in statistical learning to evaluate both stability and predictive performance. We also use the resampling technique bootstrap, which can be regarded as data self-perturbation, to evaluate the sensitivity of the modeling algorithm with respect to the specific data set. We demonstrate that the selection of the cost function plays an important role in stability. In particular, classifiers with equal misclassification costs and equal priors are less stable compared to those with unequal misclassification costs and equal priors.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Statistical Planning and Inference - Volume 137, Issue 12, 1 December 2007, Pages 3917-3929
Journal: Journal of Statistical Planning and Inference - Volume 137, Issue 12, 1 December 2007, Pages 3917-3929
نویسندگان
Panagiota Kitsantas, Myles Hollander, Lei M. Li,