.png){kind=icon}
返回
Hierarchical Independence Thresholding for learning Bayesian network classifiers
.png){kind=icon}
PRE
AI
DOI：10.1016/j.knosys.2020.106627.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Bayesian networks are powerful tools for knowledge representation and inference under conditions of uncertainty. However, learning an optimal Bayesian network classifier (BNC) is an NP-hard problem since its topology complexity increases exponentially with the number of attributes. Researchers proposed to apply information-theoretic criteria to measure conditional dependence, and independence assumptions are introduced implicitly or explicitly to simplify the network topology of BNC. In this paper, we clarify the mapping relationship between conditional mutual information and local topology, and then illustrate that informational independence does not correspond to probabilistic independence, the criterion of probabilistic independence does not necessarily hold for the independence topology. A novel framework of semi-naive Bayesian operation, called Hierarchical Independence Thresholding (HIT), is presented to efficiently identify informational conditional independence and probabilistic conditional independence by applying an adaptive thresholding method, redundant edges will be filtered out and the learned topology will fit the data better. Extensive experimental evaluation on 58 publicly available datasets reveals that when HIT is applied to BNCs (such as tree augmented Naive Bayes or k-dependence Bayesian classifier), the final BNCs achieve competitive classification performance compared to state-of-the-art learners such as Random Forest and Logistic regression. (C) 2020 Elsevier B.V. All rights reserved.
Keyword:
Bayesian network
Hierarchical independence thresholding
Informational independence
Probabilistic independence
Adaptive thresholding
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
K
IF:
7.6
论文数:
1.2W
被引数:
4.5W
查看更多机构