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基于马尔科夫边界发现的因果特征选择算法综述
吴兴宇, 江兵兵, 吕胜飞, 王翔宇, 陈秋菊, 陈欢欢
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基于马尔科夫边界发现的因果特征选择算法综述
A Survey on Causal Feature Selection Based on Markov Boundary Discovery
因果特征选择算法(也称为马尔科夫边界发现)学习目标变量的马尔科夫边界,选择与目标存在因果关系的特征,具有比传统方法更好的可解释性和鲁棒性.文中对现有因果特征选择算法进行全面综述,分为单重马尔科夫边界发现算法和多重马尔科夫边界发现算法.基于每类算法的发展历程,详细介绍每类的经典算法和研究进展,对比它们在准确性、效率、数据依赖性等方面的优劣.此外,进一步总结因果特征选择在特殊数据(半监督数据、多标签数据、多源数据、流数据等)中的改进和应用.最后,分析该领域的当前研究热点和未来发展趋势,并建立因果特征选择资料库(http://home.ustc.edu.cn/~xingyuwu/MB.html),汇总该领域常用的算法包和数据集.
Causal feature selection methods,also known as Markov boundary discovery methods, select features by learning the Markov boundary(MB) of the target variable. Hence, causal feature selection methods possess better interpretability and robustness than the traditional methods. In this paper, the existing causal feature selection methods are reviewed comprehensively. The methods are divided into two types, single MB discovery algorithms and multiple MB discovery algorithms. Based on the development history of each type, the typical algorithms as well as the recent advances are introduced in detail, and the accuracy, efficiency and data dependency of the algorithms are compared. Moreover, the extended MB discovery algorithms for special applications, including semi supervised learning, multi-label learning, multi-source learning and streaming data learning, are summarized. Finally, the current hotspots and the research directions in the future of causal feature selection are analyzed. Additionally, a toolbox for causal feature selection is developed(http://home.ustc.edu.cn/~xingyuwu/MB.html), where the commonly used packages and datasets are provided.
马尔科夫边界 / 特征选择 / 因果学习 / 因果特征选择 / 贝叶斯网络 / 马尔科夫毯 {{custom_keyword}} /
Markov Boundary / Feature Selection / Causal Learning / Causal Feature Selection / Bayesian Network / Markov Blanket {{custom_keyword}} /
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图1 马尔科夫边界的例子图2 现有MB发现算法的分类图3 直接法和分治法的区别图4 直接法的发展过程图5 分治法的发展过程图6 多重MB发现算法的发展过程/
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