RESEARCH ON LEARNING BAYESIAN NETWORK STRUCTURE BASED ON GENETIC ALGORITHMS
RESEARCH ON LEARNING BAYESIAN NETWORK STRUCTURE BASED ON GENETIC ALGORITHMS
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发表时间：
2001
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通讯作者：
Liu Da
中科院分区：
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作者：
Liu Da
由于计算的复杂性，从不完全数据中学习结构是学习贝叶斯网络的难点之一。本文提出了一种结合期望的进化算法。提出了基于期望的适应度函数，利用当前进化过程的最佳结构将不完整数据转换为完整数据，从而降低了计算复杂度，保证了算法能够向好的结构进化。给出了编码方法，设计了遗传算子，保证了算法的收敛性。实验结果表明，该算法能够有效地从不完全数据中学习贝叶斯网络结构。
Learning structure from incomplete data is one of the difficulties of learning Bayesian networks because of computational complexity. In this paper, an evolutionary algorithm combined with expectation is proposed. Fitness function is presented, which based on expectation, converts incomplete data to complete data utilizing current best structure of evolutionary process to reduce computational complexity, ensuring that this algorithm can evolve for good structure. Besides, encoding is given, and genetic operators are designed, which provides guarantee of convergence. Experimental results show that this algorithm can effectively learn Bayesian network structure from incomplete data.