朴素贝叶斯分类器:2类2特征情况下的真实误差和估计误差

2006 3rd International IEEE Conference Intelligent Systems Pub Date : 2006-09-01 DOI:10.1109/IS.2006.348481

Z. Hoare

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引用次数: 1

摘要

朴素贝叶斯(NB)分类器的低错误率被描述为令人惊讶的。已知特征的类条件独立性是NB最优性的充分条件，但不是必要条件。本研究是关于考虑特征依赖的NB估计误差与真实误差之间的差异。给出了两个二元特征的解析结果。还提供了说明示例

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Naive Bayes classifier: True and estimated errors for 2-class, 2-features case

The low error rate of naive Bayes (NB) classifier has been described as surprising. It is known that class conditional independence of the features is sufficient but not a necessary condition for optimality of NB. This study is about the difference between the estimated error and the true error of NB taking into account feature dependencies. Analytical results are derived for two binary features. Illustration examples are also provided

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2006 3rd International IEEE Conference Intelligent Systems

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