An analysis of four missing data treatment methods for supervised learning
An analysis of four missing data treatment methods for supervised learning
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DOI：
10.1080/713827181
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发表时间：
2003-05-01
影响因子：
2.8
通讯作者：
Monard, MC
中科院分区：
文献类型：
--
作者：
Batista, GEAPA;Monard, MC
数据质量方面的一个相关问题是数据缺失。尽管丢失数据问题经常发生，而且具有相关性，但许多机器学习算法都以一种相当幼稚的方式处理丢失数据。但是，缺失数据的处理应谨慎，否则可能会引入偏见的知识诱导。在这项工作中，我们分析了使用k-最近邻作为一种插补方法。插补是一个术语，表示用一些合理的值替换数据集中缺失值的过程。这种方法的一个优点是缺失数据处理与所使用的学习算法无关。这允许用户为每种情况选择最合适的插补方法。我们的分析表明，基于k-近邻算法的缺失数据填补可以优于C4.5和CN2用于处理缺失数据的内部方法，也可以优于平均值或模式填补方法，这是一种广泛用于处理缺失值的方法。
One relevant problem in data quality is missing data. Despite the frequent occurrence and the relevance of the missing data problem, many machine learning algorithms handle missing data in a rather naive way. However, missing data treatment should be carefully treated, otherwise bias might be introduced into the knowledge induced. In this work, we analyze the use of the k-nearest neighbor as an imputation method. Imputation is a term that denotes a procedure that replaces the missing values in a data set with some plausible values. One advantage of this approach is that the missing data treatment is independent of the learning algorithm used. This allows the user to select the most suitable imputation method for each situation. Our analysis indicates that missing data imputation based on the k-nearest neighbor algorithm can outperform the internal methods used by C4.5 and CN2 to treat missing data, and can also outperform the mean or mode imputation method, which is a method broadly used to treat missing values.