کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6855207 | 1437609 | 2018 | 16 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Increasing diversity in random forest learning algorithm via imprecise probabilities
ترجمه فارسی عنوان
افزایش تنوع در الگوریتم یادگیری تصادفی از طریق احتمال نامعتبر
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کلمات کلیدی
طبقه بندی، طرحهای گروهی، جنگل تصادفی احتمالات نامطلوب، معیارهای عدم قطعیت،
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
چکیده انگلیسی
Random Forest (RF) learning algorithm is considered a classifier of reference due its excellent performance. Its success is based on the diversity of rules generated from decision trees that are built via a procedure that randomizes instances and features. To find additional procedures for increasing the diversity of the trees is an interesting task. It has been considered a new split criterion, based on imprecise probabilities and general uncertainty measures, that has a clear dependence of a parameter and has shown to be more successful than the classic ones. Using that criterion in RF scheme, join with a random procedure to select the value of that parameter, the diversity of the trees in the forest and the performance are increased. This fact gives rise to a new classification algorithm, called Random Credal Random Forest (RCRF). The new method represents several improvements with respect to the classic RF: the use of a more successful split criterion which is more robust to noise than the classic ones; and an increasing of the randomness which facilitates the diversity of the rules obtained. In an experimental study, it is shown that this new algorithm is a clear enhancement of RF, especially when it applied on data sets with class noise, where the standard RF has a notable deterioration. The problem of overfitting that appears when RF classifies data sets with class noise is solved with RCRF. This new algorithm can be considered as a powerful alternative to be used on data with or without class noise.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Expert Systems with Applications - Volume 97, 1 May 2018, Pages 228-243
Journal: Expert Systems with Applications - Volume 97, 1 May 2018, Pages 228-243
نویسندگان
JoaquÃn Abellán, Carlos J. Mantas, Javier G. Castellano, SerafÃn Moral-GarcÃa,