.png){kind=icon}
返回
Improving algorithms for structure learning in Bayesian Networks using a new implicit score
.png){kind=icon}
PRE
AI
DOI：10.1016/j.eswa.2010.02.065.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Learning Bayesian Network structure from database is an NP-hard problem and still one of the most exciting challenges in machine learning. Most of the widely used heuristics search for the (locally) optimal graphs by defining a score metric and employs a search strategy to identify the network structure having the maximum score. In this work, we propose a new score (named implicit score) based on the Implicit inference framework that we proposed earlier. We then implemented this score within the K2 and MWST algorithms for network structure learning. Performance of the new score metric was evaluated on a benchmark database (ASIA Network) and a biomedical database of breast cancer in comparison with traditional score metrics BIC and BD Mutual Information. We show that implicit score yields improved performance over other scores when used with the MWST algorithm and have similar performance when implemented within K2 algorithm. (C) 2010 Elsevier Ltd. All rights reserved.
Keyword:
Bayesian Network
Implicit method
Implicit score
Structure-learning algorithm
Modeling
Breast cancer
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
IF:
7.5
论文数:
2.9W
被引数:
10.2W
机构
引用论文
.png){kind=icon}
分享收藏分享收藏Asymptotics of reaction–diffusion fronts with one static and one diffusing reactant具有一个静态和一个扩散反应物的反应扩散前沿的渐近性
OAAI
OAAI分享收藏分享收藏
查看更多内容