采用约束蚁群优化的贝叶斯网结构学习算法

王翔; 郑建国; 张超群; 阎瑞霞

您当前的位置：

首页 >

文章列表页 >

采用约束蚁群优化的贝叶斯网结构学习算法

更新时间：2025-07-09

- 采用约束蚁群优化的贝叶斯网结构学习算法
- A Constrained Ant Colony Optimization Algorithm for Learning Bayesian Networks
- 西安交通大学学报 2011年45卷第8期页码：54-61
- 作者机构：
  
  东华大学旭日工商管理学院,上海,200092
- 作者简介：
- 基金信息：
  
  : 2010-12-24. 作者简介: 王翔(1976-),男,博士生;郑建国(联系人),男,教授,博士生导师. 基金项目: 国家自然科学基金资助项目(70971020)
- DOI：
  中图分类号： TP18
- 网络首发：2011-08-10，
  
  纸质出版：2011
- 稿件说明：
移动端阅览
王翔, 郑建国, 张超群, 等. 采用约束蚁群优化的贝叶斯网结构学习算法[J]. 西安交通大学学报, 2011,45(8):54-61.

A Constrained Ant Colony Optimization Algorithm for Learning Bayesian Networks[J]. 2011, 45(8): 54-61.
王翔, 郑建国, 张超群, 等. 采用约束蚁群优化的贝叶斯网结构学习算法[J]. 西安交通大学学报, 2011,45(8):54-61. DOI：

A Constrained Ant Colony Optimization Algorithm for Learning Bayesian Networks[J]. 2011, 45(8): 54-61. DOI：

摘要

针对贝叶斯网络结构学习问题

提出了一种约束蚁群优化算法.该算法根据贝叶斯得分的局部一致性原理设计了一种增边规则

并且将此规则引入蚁群算法的框架中

从而实现了在搜索过程中利用启发式信息动态缩减搜索空间、同时减少运行时间的目的.此外

还从理论上证明了增边规则的正确性

而且从实验角度讨论了约束蚁群优化算法的参数敏感性.实验结果表明

在解决较大规模的贝叶斯网络结构学习问题时

约束蚁群优化算法在保证求解精度的条件下比蚁群优化算法的运行时间减少40%以上.

Abstract

A novel constrained ant colony optimization algorithm is proposed for learning Bayesian networks. An add-edge-rule is designed in the proposed algorithm based on the locally consistent scoring criterion of BDEu metric. The add-edge-rule is embedded into the frame of ant colony optimization so that the heuristic information can be dynamically used to restrict the candidate evaluations during the search process and the running time of the new algorithm can be reduced. In addition

the correctness of the add-edge-rule is proved in theory and the parameter sensitivity of the constrained ant colony optimization is analyzed in experiments. Empirical tests show that without loss of results accuracy

the convergence speed of the proposed algorithm is at least 40% faster than that of the ant colony optimization algorithm for large-scale learning Bayesian networks.

关键词

Keywords

references

HECKERMAN D. Bayesian networks for data mining[J]. Data Mining and Knowledge Discovery,1997, 1(1): 79-119.

CHENG J, GREINER R, KELLY J, et al. Learning Bayesian networks from data: an information-theory based approach[J]. Artificial Intelligence, 2002, 137(1/2): 43-90.

DE CAMPOS L M, FERN N J M, MEZ G J A, et al. Ant colony optimization for learning Bayesian networks[J]. International Journal of Approximate Reasoning, 2002, 31(3): 291-311.

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

JI J, ZHANG H, HU R, et al. Bayesian network learning algorithm based on independence test and ant colony optimization[J]. Acta Automatica Sinica, 2009, 35: 281-288.

CHICKERING D M. Optimal structure identification with greedy search[J]. The Journal of Machine Learning Research, 2003, 3: 554.

DORIGO M, MANIEZZO V, COLORNI A. Ant system: optimization by a colony of cooperating agents[J]. Systems, Man, and Cybernetics: Part B Cybernetics, 2002, 26(1): 29-41.

张云,冯博琴,麻首强,等. 蚁群-遗传融合的文本聚类算法[J]. 西安交通大学学报, 2007, 41(10): 1146-1150.

ZHANG Yun, Feng Boqin, MA Shouqiang, et al. Text clustering based on fusion of ant colony and genetic algorithms[J]. Journal of Xi'an Jiaotong University, 2007, 41(10): 1146-1150.

任志刚,冯祖仁,柯良军. 蚁群优化属性约简算法[J]. 西安交通大学学报, 2008, 42(4): 440-444.

REN Zhigang, Feng Zuren, KE Liangjun. Ant colony optimization approach to attribute reduction problem[J]. Journal of Xi'an Jiaotong University, 2008, 42(4): 440-444.

郑巍,刘三阳,寇晓丽. 一种面向传感器网络的蚁群优化路径恢复算法[J]. 西安交通大学学报, 2010, 44(1): 83-86.

ZHENG Wei, LIU Sanyang, KOU Xiaoli. A route restoration algorithm for sensor network via ant colony optimization[J]. Journal of Xi'an Jiaotong University, 2010, 44(1): 83-86.

张兆军,冯祖仁,任志刚. 采用序优化的改进蚁群算法[J]. 西安交通大学学报, 2010, 44(2): 15-19.

ZHANG Zhaojun, FENG Zuren, REN Zhigang. Novel ant colony optimization algorithm based on order optimization[J]. Journal of Xi'an Jiaotong University, 2010, 44(2): 15-19.

DE CAMPOS L M, PUERTA J M. Stochastic local and distributed search algorithms for learning belief networks[C]∥Proceedings of the III International Symposium on Adaptive Systems: Evolutionary Computation and Probabilistic Graphical Model. Havana, Cuba: ICIMAF, 2001: 109-115.

HECKERMAN D, GEIGER D, CHICKERING D M. Learning Bayesian networks: the combination of knowledge and statistical data[J]. Machine Learning, 1995, 20(3): 197-243.

GAMES J A, MATEO J L, PUERTA J M. Learning Bayesian networks by hill climbing: efficient methods based on progressive restriction of the neighborhood[J]. Data Mining and Knowledge Discovery, 2011,22(1): 106-148.

动态团队定向问题的模型及其优化算法.西安交通大学学报,2011,45(6):54-58.

一种高效的对等网络流媒体数据调度算法.西安交通大学学报,2011,45(6):20-26.

一种修复网络拓扑的Steiner树移动控制算法.西安交通大学学报,2011,45(2):39-43.

可演化网络中移动代理的性能分析及优化方法.西安交通大学学报,2010,44(12): 5-9.

利用蚁群优化的非均匀分簇无线传感器网络路由算法.西安交通大学学报,2010,44(6):33-38.

采用序优化的改进蚁群算法.西安交通大学学报,2010,44(2):15-19.

一种面向传感器网络的蚁群优化路径恢复算法.西安交通大学学报,2010,44(1): 83-86.

一种可演化网络的研究与实践.西安交通大学学报,2008,42(10):1193-1203.

蚁群优化属性约简算法.西安交通大学学报,2008,42(4):440-444.

用于无线传感器网络的比例公平队列调度算法.西安交通大学学报,2008,42(2):129-132.

蚁群-遗传融合的文本聚类算法.西安交通大学学报,2007,41(10):1146-1150.

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

语义区分和子图整合驱动的知识图谱表示学习方法

超临界二氧化碳向心透平轮背间隙内风阻损失及流动特性研究

EBSILON软件在热电联产系统优化中的应用综述

冷库水平空气幕热湿阻隔性能数值模拟研究

采用试验设计方法的第三代CO2气体箔轴承箔片几何参数敏感性分析