A new hybrid method for learning bayesian networks: Separation and reunion
Publication in refereed journal

替代計量分析
.

其它資訊
摘要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.
著者Hui Liu, Shuigeng Zhou, Wai Lam, Jihong Guan
期刊名稱Knowledge-Based Systems
出版年份2017
月份4
日期1
卷號121
出版社Elsevier
頁次185 - 197
國際標準期刊號0950-7051
電子國際標準期刊號1872-7409
語言美式英語
關鍵詞Bayesian network, Conditional independence, Scoring function, d-separation

上次更新時間 2021-19-01 於 01:18