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What Are the Differences Between Bayesian Classifiers and Mutual-Information Classifiers?

机译：贝叶斯分类器和互信息分类器有什么区别？

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摘要

In this paper, both Bayesian and mutual-information classifiers are examined for binary classifications with or without a reject option. The general decision rules are derived for Bayesian classifiers with distinctions on error types and reject types. A formal analysis is conducted to reveal the parameter redundancy of cost terms when abstaining classifications are enforced. The redundancy implies an intrinsic problem of nonconsistency for interpreting cost terms. If no data are given to the cost terms, we demonstrate the weakness of Bayesian classifiers in class-imbalanced classifications. On the contrary, mutual-information classifiers are able to provide an objective solution from the given data, which shows a reasonable balance among error types and reject types. Numerical examples of using two types of classifiers are given for confirming the differences, including the extremely class-imbalanced cases. Finally, we briefly summarize the Bayesian and mutual-information classifiers in terms of their application advantages and disadvantages, respectively.
机译：在本文中，检查了贝叶斯分类器和互信息分类器是否具有拒绝选项的二进制分类。贝叶斯分类器的通用决策规则是在错误类型和拒绝类型上有所区别的。进行正式分析以揭示强制弃权分类时成本术语的参数冗余。冗余隐含着一个固有的问题，即解释成本条件时不一致。如果没有有关成本项的数据，我们将证明贝叶斯分类器在类不平衡分类中的弱点。相反，互信息分类器能够根据给定的数据提供客观的解决方案，从而显示出错误类型和拒绝类型之间的合理平衡。给出了使用两种类型的分类器的数值示例，以确认差异，包括极端不平衡的情况。最后，我们分别从应用程序的优缺点出发，简要概述了贝叶斯分类器和互信息分类器。

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