کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
505727 | 864532 | 2009 | 8 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Hedged predictions for traditional Chinese chronic gastritis diagnosis with confidence machine
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
نرم افزارهای علوم کامپیوتر
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چکیده انگلیسی
Most classifiers output predictions for new instances without indicating how reliable they could be. Transductive confidence machine (TCM) is a novel framework that provides hedged prediction coupled with valid confidence. Many popular machine learning algorithms can be transformed into the framework of TCM, and therefore be used for producing hedged predictions. This paper incorporates random forest (RF) to propose a method named TCM-RF for classification of chronic gastritis data. Our method benefits from TCM-RF's high performance when features are noisy, highly correlated and of mixed types. The experimental results show that TCM-RF produces informative as well as effective predictions.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computers in Biology and Medicine - Volume 39, Issue 5, May 2009, Pages 425–432
Journal: Computers in Biology and Medicine - Volume 39, Issue 5, May 2009, Pages 425–432
نویسندگان
Huazhen Wang, Chengde Lin, Fan Yang, Xueqin Hu,