Discrete Bayesian Network Classifiers: A Survey
Discrete Bayesian Network Classifiers: A Survey
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DOI：
10.1145/2576868
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发表时间：
2014-07-01
影响因子：
16.6
通讯作者：
Larranaga, Pedro
中科院分区：
文献类型：
--
作者：
Bielza, Concha;Larranaga, Pedro
自1960年朴素贝叶斯模型首次被引入以来，我们不得不等待30多年，才迎来了所谓的贝叶斯网络分类器的复兴。基于贝叶斯网络，这些分类器具有许多优势，如模型可解释性，适应复杂的数据和分类问题设置，存在有效的学习和分类任务算法，以及在现实世界问题中的成功适用性。在这篇文章中，我们调查了迄今为止设计的一整套离散贝叶斯网络分类器，按结构复杂性的递增顺序组织：朴素贝叶斯，选择性朴素贝叶斯，语义贝叶斯，一依赖贝叶斯分类器，k依赖贝叶斯分类器，贝叶斯网络增强朴素贝叶斯，基于马尔可夫毯子的贝叶斯分类器，无限制贝叶斯分类器和贝叶斯多元。特征子集选择、生成和判别结构和参数学习的问题也被涵盖。
We have had to wait over 30 years since the naive Bayes model was first introduced in 1960 for the so-called Bayesian network classifiers to resurge. Based on Bayesian networks, these classifiers have many strengths, like model interpretability, accommodation to complex data and classification problem settings, existence of efficient algorithms for learning and classification tasks, and successful applicability in real-world problems. In this article, we survey the whole set of discrete Bayesian network classifiers devised to date, organized in increasing order of structure complexity: naive Bayes, selective naive Bayes, seminaive Bayes, one-dependence Bayesian classifiers, k-dependence Bayesian classifiers, Bayesian network-augmented naive Bayes, Markov blanket-based Bayesian classifier, unrestricted Bayesian classifiers, and Bayesian multinets. Issues of feature subset selection and generative and discriminative structure and parameter learning are also covered.