.png){kind=icon}
返回
Empirical hardness of finding optimal Bayesian network structures: algorithm selection and runtime prediction
.png){kind=icon}
OA
AI
DOI：10.1007/s10994-017-5680-2.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Various algorithms have been proposed for finding a Bayesian network structure that is guaranteed to maximize a given scoring function. Implementations of state-of-the-art algorithms, solvers, for this Bayesian network structure learning problem rely on adaptive search strategies, such as branch-and-bound and integer linear programming techniques. Thus, the time requirements of the solvers are not well characterized by simple functions of the instance size. Furthermore, no single solver dominates the others in speed. Given a problem instance, it is thus a priori unclear which solver will perform best and how fast it will solve the instance. We show that for a given solver the hardness of a problem instance can be efficiently predicted based on a collection of non-trivial features which go beyond the basic parameters of instance size. Specifically, we train and test statistical models on empirical data, based on the largest evaluation of state-of-the-art exact solvers to date. We demonstrate that we can predict the runtimes to a reasonable degree of accuracy. These predictions enable effective selection of solvers that perform well in terms of runtimes on a particular instance. Thus, this work contributes a highly efficient portfolio solver that makes use of several individual solvers.
Keyword:
Bayesian networks
Structure learning
Algorithm selection
Hyperparameter optimization
Empirical hardness
Algorithm portfolio
Runtime prediction
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:
2.9
论文数:
2.7K
被引数:
3.4W
机构
引用论文
分享收藏分享收藏Being Bayesian about network structure. A Bayesian approach to structure discovery in Bayesian networks关于网络结构的贝叶斯。贝叶斯网络中结构发现的贝叶斯方法.png)
MACHINE LEARNINGOAAI
MACHINE LEARNINGIF2.9
OAAI分享收藏分享收藏
查看更多内容