Missing Data Imputation for Categorical Variables

IF 0.3 Q4 ECONOMICS Statistika-Statistics and Economy Journal Pub Date : 2022-09-01 DOI:10.54694/stat.2022.3
Jaroslav Horníček, H. Řezanková
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引用次数: 0

Abstract

Dealing with missing data is a crucial part of everyday data analysis. The IMIC algorithm is a missing data imputation method that can handle mixed numerical and categorical datasets. However, the categorical data are crucial for this work. This paper proposes the new improvement of the IMIC algorithm. The two proposed modifications consider the number of categories in each categorical variable. Based on this information, the factor, which modifies the original measure, is computed. The factor equation is inspired by the Eskin similarity measure that is known in the hierarchical clustering of categorical data. The results show that as the missing value ratio in the dataset grows, better results are achieved using the second modification. The paper also shortly analyzes the advantages and disadvantages of using the IMIC algorithm.
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范畴变量的缺失数据推断
处理缺失数据是日常数据分析的关键部分。IMIC算法是一种能够处理数值和分类混合数据集的缺失数据补全方法。然而,分类数据对这项工作至关重要。本文提出了对IMIC算法的新改进。这两种建议的修改考虑了每个分类变量中类别的数量。根据这些信息,计算修改原始度量的因子。因子方程的灵感来自于在分类数据的层次聚类中已知的Eskin相似性度量。结果表明,随着数据集缺失值比的增大,使用第二次修正可以获得更好的结果。本文还简要分析了采用IMIC算法的优缺点。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
0.60
自引率
0.00%
发文量
23
审稿时长
24 weeks
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