A heuristic method for learning Bayesian networks using discrete particle swarm optimization
A heuristic method for learning Bayesian networks using discrete particle swarm optimization
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DOI：
10.1007/s10115-009-0239-6
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发表时间：
2010-08
影响因子：
2.7
通讯作者：
Tong Wang;Jie Yang
中科院分区：
文献类型：
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作者：
Tong Wang;Jie Yang
贝叶斯网络是一种在不确定条件下表示和推理的强大方法。许多研究者的目标是找到从数据中学习贝叶斯网络的好算法。而启发式搜索算法是其中最有效的算法之一。由于可能的结构数量随着变量的数量呈指数增长，通过穷尽地考虑所有可能的结构从数据中学习模型结构是不可行的。粒子群优化算法（PSO）是一种功能强大的启发式最优搜索算法，已广泛应用于各个领域。遗憾的是，经典的粒子群算法只适用于连续和实值空间，而贝叶斯网络的学习问题是在离散空间。本文引入了速度和位置更新规则的两种修改，提出了一种基于二进制粒子群算法的贝叶斯网络学习方法。实验结果表明，由于只需要较少的代数就可以获得最优的贝叶斯网络结构，因此该方法具有更高的效率。在比较中，该方法优于其他启发式方法，如遗传算法和经典二值粒子群算法。
Bayesian networks are a powerful approach for representing and reasoning under conditions of uncertainty. Many researchers aim to find good algorithms for learning Bayesian networks from data. And the heuristic search algorithm is one of the most effective algorithms. Because the number of possible structures grows exponentially with the number of variables, learning the model structure from data by considering all possible structures exhaustively is infeasible. PSO (particle swarm optimization), a powerful optimal heuristic search algorithm, has been applied in various fields. Unfortunately, the classical PSO algorithm only operates in continuous and real-valued space, and the problem of Bayesian networks learning is in discrete space. In this paper, two modifications of updating rules for velocity and position are introduced and a Bayesian networks learning based on binary PSO is proposed. Experimental results show that it is more efficient because only fewer generations are needed to obtain optimal Bayesian networks structures. In the comparison, this method outperforms other heuristic methods such as GA (genetic algorithm) and classical binary PSO.