کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
4944132 1437979 2018 24 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Regression learning based on incomplete relationships between attributes
ترجمه فارسی عنوان
یادگیری رگرسیون مبتنی بر روابط ناقص بین ویژگی ها
کلمات کلیدی
ویژگی روابط؛ شبکه منطقی مارکوف؛ Interpretability؛ پیش بینی مرتبه اول؛ مشکل رگرسیون
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
چکیده انگلیسی


- Complete relationships are obtained from incomplete relationships by inference.
- Complete relationships can restrain the curve shape of regression model.
- The method can hold competitive generalization performance of regression model.
- The method can improve the interpretability of regression model.

In recent years, machine learning researchers have focused on methods to construct flexible and interpretable regression models. However, the method of obtaining complete knowledge from incomplete and fuzzy prior knowledge and the trade-off between the generalization performance and the interpretability of the model are very important factors to consider. In this paper, we propose a new regression learning method. Complete relationships are obtained from the incomplete fuzzy relationships between attributes by using Markov logic networks [29]. The complete relationships are then applied to constrain the shape of the regression model in the optimization procedure to solve the trade-off problem. Finally, the benefits of our approach are illustrated on benchmark data sets and in real-world experiments.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Information Sciences - Volume 422, January 2018, Pages 408-431
نویسندگان
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