کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4946275 | 1439280 | 2017 | 13 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
A new hybrid method for learning bayesian networks: Separation and reunion
ترجمه فارسی عنوان
یک روش ترکیبی جدید برای یادگیری شبکه های بیزی: جداسازی و پیوستن
دانلود مقاله + سفارش ترجمه
دانلود مقاله ISI انگلیسی
رایگان برای ایرانیان
کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
چکیده انگلیسی
Most existing algorithms for learning Bayesian networks (BNs) can be categorized as constraint-based or score-based methods. Constraint-based algorithms use conditional independence (CI) tests to judge the presence or absence of an edge. Though effective and applicable to (high-dimensional data) large-scale networks, CI tests require a large number of samples to determine the independencies. Thus they can be unreliable especially when the sample size is small. On the other hand, score-based methods employ a score metric to evaluate each candidate network structure, but they are inefficient in learning large-scale networks due to the extremely large search space. In this paper, we propose a new hybrid Bayesian network learning method, SAR (the abbreviation of Separation And Reunion), which maintains the merits of both types of learning techniques while avoiding their drawbacks. Extensive experiments show that our method generally achieves better performance than state-of-the-art methods.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Knowledge-Based Systems - Volume 121, 1 April 2017, Pages 185-197
Journal: Knowledge-Based Systems - Volume 121, 1 April 2017, Pages 185-197
نویسندگان
Hui Liu, Shuigeng Zhou, Wai Lam, Jihong Guan,