Naive Bayes as an imputation tool for classification problems
Naive Bayes as an imputation tool for classification problems
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DOI：
10.1109/ichis.2005.78
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发表时间：
2005-12
期刊：
影响因子：
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通讯作者：
Antonio J. T. Garcia;Eduardo R. Hruschka
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文献类型：
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作者：
Antonio J. T. Garcia;Eduardo R. Hruschka
我们研究了朴素贝叶斯分类器作为分类问题的归算工具的使用，详细说明了为什么通常使用的多数方法可能会在分类上下文中插入偏差。考虑到Rubin对缺失分布的类型学，我们已经进行了实验，说明了一个输入过程如何影响分类任务。我们的结果表明，由朴素贝叶斯执行的估算对其他分类器（决策树和最近邻）也很有用。从这个意义上说，可以推导出有趣的混合系统来对缺失值的数据集进行分类。
We investigate the use of the naive Bayes classifier as an imputation tool for classification problems, elaborating on why the usually employed majority method may insert biases in a classification context. Considering Rubin's typology for the distribution of missingness, we have performed experiments that illustrate how an imputation process may influence classification tasks. Our results show that imputations performed by the naive Bayes can be useful for other classifiers (decision trees and nearest neighbors). In this sense, interesting hybrid systems to classify datasets with missing values can be derived.