【摘要】 从不完备数据中学习网络结构是 Bayesian网学习的难点之一 ,计算复杂度高 ,实现困难 .针对该问题提出了一种进化算法 .设计了结合数学期望的适应度函数 ,该函数利用进化过程中的最好 Bayesian网把不完备数据转换成完备数据 ,从而大大简化了学习的复杂度 ,并保证算法能够向好的结构不断进化 .此外 ,给出了网络结构的编码方案 ,设计了相应的遗传算子 ,使得该算法能够收敛到全局最优的 Bayesian网结构 .模拟实验结果表明 ,该算法能有效地从不完备数据中学习 .更多还原

【Abstract】 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.更多还原