Research Article
KTOBS: An Approach of Bayesian Network Learning Based on K-tree Optimizing Ordering-Based Search
@INPROCEEDINGS{10.1007/978-3-030-92635-9_5, author={Qingwang Zhang and Sihang Liu and Ruihong Xu and Zemeng Yang and Jianxiao Liu}, title={KTOBS: An Approach of Bayesian Network Learning Based on K-tree Optimizing Ordering-Based Search}, proceedings={Collaborative Computing: Networking, Applications and Worksharing. 17th EAI International Conference, CollaborateCom 2021, Virtual Event, October 16-18, 2021, Proceedings, Part I}, proceedings_a={COLLABORATECOM}, year={2022}, month={1}, keywords={Bayesian network Candidate parent node k-tree Ordering-based search}, doi={10.1007/978-3-030-92635-9_5} }- Qingwang Zhang
Sihang Liu
Ruihong Xu
Zemeng Yang
Jianxiao Liu
Year: 2022
KTOBS: An Approach of Bayesian Network Learning Based on K-tree Optimizing Ordering-Based Search
COLLABORATECOM
Springer
DOI: 10.1007/978-3-030-92635-9_5
Abstract
How to construct Bayesian Networks (BN) efficiently and accurately is a research hotspot in the era of artificial intelligence. By limiting the tree-width of the network, the Bayesian network learning based onk-tree can be used to process large-scale of variables. However, this method has the problems of low accuracy, further to optimize the order of adding nodes,etc. In order to solve these problems, this work proposes a Bayesian learning method based on k-tree optimizing ordering-based search (KTOBS). Firstly, the local learning search strategy is adopted to obtain the candidate parent sets of each variable efficiently and accurately. Then it selectsk+ 1 nodes based on the obtained candidate parent node sets, and constructs the corresponding initial sub-network. Then the heuristic evaluation strategy is used to add subsequent nodes successively, and thus to get the initial network. Finally, it optimizes the network iteratively through switching nodes until the score of network no longer increases. The experimental results show thatKTOBScan learn a network structure with higher accuracy than otherk-tree algorithms in a given limited time.
Availability and implementation: codes and experiment dataset are available at:http://122.205.95.139/KTOBS/.
