Learning Bayesian Network Structure from Node Ordering Searching Optimal

LIU Bin; WANG Haiyu; SUN Meiting; LIU Haoran; IU Yongji; HANG Chunlan

doi:10.11999/JEIT170675

Volume 40 Issue 5

May 2018

Turn off MathJax

Article Contents

Article Navigation > Journal of Electronics & Information Technology > 2018 > 40(5): 1234-1241

LIU Bin, WANG Haiyu, SUN Meiting, LIU Haoran, IU Yongji, HANG Chunlan. Learning Bayesian Network Structure from Node Ordering Searching Optimal[J]. Journal of Electronics & Information Technology, 2018, 40(5): 1234-1241. doi: 10.11999/JEIT170675

Citation:

LIU Bin, WANG Haiyu, SUN Meiting, LIU Haoran, IU Yongji, HANG Chunlan. Learning Bayesian Network Structure from Node Ordering Searching Optimal[J]. Journal of Electronics & Information Technology, 2018, 40(5): 1234-1241. doi: 10.11999/JEIT170675

Citation:

PDF( 1033 KB)

Learning Bayesian Network Structure from Node Ordering Searching Optimal

doi: 10.11999/JEIT170675

Funds:

The National Natural Science Foundation of China (51641609)

Received Date: 2017-07-07
Rev Recd Date: 2017-11-29
Publish Date: 2018-05-19

Abstract

Abstract

The performance of the K2 algorithm depends on node ordering heavily, and the genetic algorithm can not find the node ordering effectively. For these problems, a new Bayesian structure learning algorithm, named NOK2 (Node Ordering searching for K2 algorithm), is proposed to solve the Bayesian structure learning problem by searching node ordering directly. According to the requirements of K2 algorithm based on prior knowledge and the weight matrix of spanning tree, the fitness function for quantitative evaluation of node ordering is established. The genetic algorithm is redesigned by a new method combines the dynamic learning constants, the hybrid crossover strategy, the inverted mutation strategy and the isolated node processing, so that the algorithm can find the node order of the highest fitness value, and this node sequence is taken as a prior knowledge of the K2 algorithm to obtain the optimal Bayesian network structure. Compared with other optimization algorithms, experimental results indicate that the NOK2 algorithm can significantly increase nearly 13.11% in the scoring metric values.
- Bayesian network structure,
- Node ordering search,
- Fitness function of node sequence,
- K2 algorithm

FullText(HTML)

References(26)

References

刘广怡, 李鸥, 宋涛, 等. 基于贝叶斯网络的无线传感网高效数据传输方法[J]. 电子与信息学报, 2016, 38(6): 1362-1367. doi: 10.11999/JEIT151027.

TIEN I and KIUREGHIAN A D. Algorithms for Bayesian network modeling and reliability assessment of infrastructure systems[J]. Reliability Engineering System Safety, 2016, 156: 134-147. doi: 10.1016/j.ress.2016.07.022.

LIU Guangyi, LI Ou, SONG Tao, et al. Energy-efficiency data transmission method in WSN based on Bayesian network[J]. Journal of Electronics Information Technology, 2016, 38(6): 1362-1367. doi: 10.11999/JEIT151027.

QIU H, WEI Z, LIU Y, et al. A Bayesian network meta- analysis of three different surgical procedures for the treatment of humeral shaft fractures[J]. Medicine, 2016, 95(51): e5464. doi: 10.1097/MD.0000000000005464.

邓歆, 孟洛明. 基于贝叶斯网络的通信网告警相关性和故障诊断模型[J]. 电子与信息学报, 2007, 29(5): 1182-1186. doi: 10.3724/SP.J.1146.2005.01290.

DENG Xin and MENG Luoming. Bayesian networks based alarm correlation and fault diagnosis in communication networks[J]. Journal of Electronics Information Technology, 2007, 29(5): 1182-1186. doi: 10.3724 /SP.J.1146.2005.01290.

CHICKERING D M. Learning Bayesian networks is NP- complete[J]. Networks, 1996, 112(2): 121-130. doi: 10.1007/ 978-1-4612-2404-4_12.

CARRIGER J F, MARTIN T M, and BARRON M G. A Bayesian network model for predicting aquatic toxicity mode of action using two dimensional theoretical molecular descriptors[J]. Aquatic Toxicology, 2016, 180: 11-24. doi: 10.1016/j.aquatox.2016.09.006.

ROH M C and LEE S W. Human gesture recognition using a simplified dynamic Bayesian network[J]. Multimedia Systems, 2015, 21(6): 557-568. doi: 10.1007/s00530-014-0414-9.

CHEN X W, ANANTHA G, and LIN X. Improving Bayesian network structure learning with mutual information based node ordering in the K2 algorithm[J]. IEEE Transactions on Knowledge Data Engineering, 2007, 20(5): 628-640. doi: 10.1109/TKDE.2007.190732.

SONG K and KIM D W. An efficient node ordering method using the conditional frequency for the K2 algorithm[J]. Pattern Recognition Letters, 2014, 40(4): 80-87. doi: 10.1016/ j.patrec.2013.12.021.

刘浩然, 孙美婷, 李雷, 等. 基于蚁群节点寻优的贝叶斯网络结构算法研究[J]. 仪器仪表报, 2017, 38(1): 143-150. doi: 10.3969/j.issn.0254-3087.2017.01.019.

LIU Haoran, SUN Meiting, LI Lei, et al. Bayesian network structure learning algorithm based on ant colony optimization search optimal node ordering[J]. Chinese Journal of Scientific Instrument, 2017, 38(1): 143-150. doi: 10.3969/j.issn.0254-3087.2017.01.019.

KRUSKAL J B. On the shortest spanning subtree of a graph and the traveling salesman problem[J]. Proceedings of the American Mathematical Society, 1956, 7(1): 48-50. doi: 10.2307/2033241.

HU R S. A hybrid PSO-GA algorithm for job shop scheduling in machine tool production[J]. International Journal of Production Research, 2015, 53(19): 1-27. doi: 10.1080/ 00207543.2014.994714.

KPPPMAN R and WANG S. Mutual information based labelling and comparing clusters[J]. Scientometrics, 2017, 111(2): 1157-1167. doi: 10.1007/s11192-017-2305-2.

COOPER G F and HERSKOVITS E. A Bayesian method for the induction of probabilistic networks from data[J] Machine Learning, 1992, 9(4): 309-347. doi: 10.1007/BF00994110.

LIN S and KERNIGHAN B W. An effective heuristic algorithm for the TSP[J]. Operations Research, 1973, 21(2): 498-516. doi: 10.1287/opre.21.2.498.

刘广怡, 李鸥, 张大龙. 一种通过结构边界进行贝叶斯网络学习的算法[J].电子与信息学报, 2015, 37(4): 894-899. doi: 10.11999/JEIT140786.

LIU Guangyi, LI Ou, and ZHANG Dalong. Learning Bayesian network from structure boundaries[J]. Journal of Electronics Information Technology, 2015, 37(4): 894-899. doi: 10.11999/JEIT140786.

SCHWARZ G. Estimating dimension of a model[J]. Annals of Statistics, 1978, 6(2): 461-464. doi: 10.1214/aos/1176344136.

BEINLICH I A, SUERMONDT H J, CHAVEZ R M, et al. The ALARM monitoring mystem: A case study with two probabilistic inference techniques for belief networks[J]. Lecture Notes in Medical Informatics, 1989, 38: 247-256. doi: 10.1007/978-3-642-93437-7_28.

MAJUMDAR J and BHUNIA A K. Genetic algorithm for asymmetric traveling salesman problem with imprecise travel times[J]. Journal of Computational Applied Mathematics, 2011, 235(9): 3063-3078. doi: 10.1016 /j.carm.2010.12.027.

TSAMARDINOS I, BROWN L E, and ALIFERIS C F. The max-min hill-climbing Bayesian network structure learning algorithm[J]. Machine Learning, 2006, 65(1): 31-78. doi: 10.1007/s10994-006-6889-7.

LARRAAGAL P, POZA M, YURRAMENDI Y, et al. Structure learning of bayesian networks by genetic algorithms: A performance analysis of control parameters[J]. IEEE Transactions on Pattern Analysis Machine Intelligence, 1996, 18(9): 912-926. doi: 10.1109/34.537345.

NIE S, CAMPOS C P D, and JI Q. Efficient learning of Bayesian networks with bounded tree-width[J]. International Journal of Approximate Reasoning, 2016, 80: 412-427. doi: 10.1016/j.ijar.2016.07.002.

Relative Articles

Supplements(0)

Cited By

Proportional views