论文标题
用于学习贝叶斯网络的全面改进的混合算法：多个复合内存擦除
A Comprehensively Improved Hybrid Algorithm for Learning Bayesian Networks: Multiple Compound Memory Erasing
论文作者
论文摘要
使用贝叶斯网络分析节点之间的因果关系是一个热点。现有的网络学习算法主要是基于约束和基于得分的网络生成方法。基于约束的方法主要是有条件独立性（CI）测试的应用，但是在高维度和小样本的情况下，CI测试的不准确性一直是基于约束方法的问题。基于得分的方法使用评分功能和搜索策略来找到最佳的候选网络结构，但是随着节点数量的增加，搜索空间增加了太多，并且学习效率非常低。本文提出了一种新的混合算法，MCME（多个复合内存擦除）。该方法保留了前两种方法的优势，解决了上述CI测试的缺点，并在方向歧视阶段中的评分函数中创新。大量实验表明，与某些现有算法相比，MCME具有更好或类似的性能。
Using a Bayesian network to analyze the causal relationship between nodes is a hot spot. The existing network learning algorithms are mainly constraint-based and score-based network generation methods. The constraint-based method is mainly the application of conditional independence (CI) tests, but the inaccuracy of CI tests in the case of high dimensionality and small samples has always been a problem for the constraint-based method. The score-based method uses the scoring function and search strategy to find the optimal candidate network structure, but the search space increases too much with the increase of the number of nodes, and the learning efficiency is very low. This paper presents a new hybrid algorithm, MCME (multiple compound memory erasing). This method retains the advantages of the first two methods, solves the shortcomings of the above CI tests, and makes innovations in the scoring function in the direction discrimination stage. A large number of experiments show that MCME has better or similar performance than some existing algorithms.
