What Are the Differences Between Bayesian Classifiers and Mutual-Information Classifiers?
What Are the Differences Between Bayesian Classifiers and Mutual-Information Classifiers?
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贝叶斯分类器和互信息分类器有什么区别？
DOI：
10.1109/tnnls.2013.2274799
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发表时间：
2014-02-01
影响因子：
10.4
通讯作者：
Hu, Bao-Gang
中科院分区：
文献类型：
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作者：
Hu, Bao-Gang
在这篇文章中，贝叶斯和互信息分类器都被检查有或没有拒绝选项的二进制分类。给出了区分错误类型和拒绝类型的贝叶斯分类器的一般决策规则。通过形式化分析揭示了强制分类时成本项的参数冗余性。这种冗余意味着在解释成本术语时存在内在的不一致性问题。如果没有给出代价项的数据，我们证明了贝叶斯分类器在类不平衡分类中的弱点。相反，互信息分类器能够从给定的数据中提供客观的解决方案，这表明错误类型和拒绝类型之间的合理平衡。文中给出了使用两种分类器的数值例子，包括类极不平衡的情况。最后，简要总结了贝叶斯分类器和互信息分类器各自的应用优势和不足。
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.