Bayesian network classifiers
Bayesian network classifiers
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DOI：
10.1023/a:1007465528199
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发表时间：
1997-11-01
期刊：
影响因子：
7.5
通讯作者：
Goldszmidt, M
中科院分区：
文献类型：
--
作者：
Friedman, N;Geiger, D;Goldszmidt, M
最近在监督学习方面的工作表明，一个令人惊讶的简单贝叶斯分类器具有很强的特征独立性假设，称为朴素贝叶斯，与最先进的分类器（如C4.5）竞争。这一事实提出了一个问题，即具有较少限制性假设的分类器是否可以表现得更好。在本文中，我们评估的方法，从数据中诱导分类，贝叶斯网络学习理论的基础上。这些网络是概率分布的因子化表示，概括了朴素贝叶斯分类器并显式表示了关于独立性的陈述。在这些方法中，我们选出了一种称为树增强朴素贝叶斯（TAN）的方法，它的性能优于朴素贝叶斯，但同时保持了朴素贝叶斯所特有的计算简单性（不涉及搜索）和鲁棒性。我们实验性地休息这些方法，使用的问题，从加州大学欧文分校的知识库，并比较它们的C4.5，朴素贝叶斯，包装方法的功能选择。
Recent work in supervised learning has shown that a surprisingly simple Bayesian classifier with strong assumptions of independence among features, called naive Bayes is competitive with state-of-the-art classifiers such as C4.5. This fact raises the question of whether a classifier with less restrictive assumptions can perform even better. In this paper we evaluate approaches for inducing classifiers from data, based on the theory of learning Bayesian networks. These networks are factored representations of probability distributions that generalize the naive Bayesian classifier and explicitly represent statements about independence. Among these approaches we single out a method we call Tree Augmented Naive Bayes (TAN), which outperforms naive Bayes, yet at the same time maintains the computational simplicity (no search involved) and robustness that characterize naive Bayes. We experimentally rested these approaches, using problems from the University of California at Irvine repository, and compared them to C4.5, naive Bayes, and wrapper methods for feature selection.