[1]袁建裕,闫春艳,叶志伟,等. 离散型缺失数据填补法综合比较[J].湖北工业大学学报,2021,(5):59-63.
　YUAN Jianyu,YAN Chunyan,YE Zhiwei,et al. Comprehensive Comparison of Methods for Imputing Discrete Missing Data[J].,2021,(5):59-63.
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 离散型缺失数据填补法综合比较()
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《湖北工业大学学报》[ISSN:1003-4684/CN:42-1752/Z]

卷:
期数:
2021年第5期
页码:
59-63
栏目:
湖北工业大学学报
出版日期:
2021-10-31

文章信息/Info

Title:
 Comprehensive Comparison of Methods for Imputing Discrete Missing Data
文章编号:
1003-4684(2021)05-0059-05
作者:
 袁建裕1;  闫春艳1;  叶志伟1;  杨志勇2
 1 湖北工业大学计算机学院， 湖北 武汉 430068；
2 湖北省公安厅科技信息处， 湖北 武汉 430064
Author(s):
 YUAN Jianyu1; YAN Chunyan1; YE Zhiwei1; YANG Zhiyong2
 1 School of Computer Science, Hubei Univ. of Tech., Wuhan 430068, China; 
2 Hubei Provincial Public Security Department, Wuhan 430064, China
关键词:
 ; 离散缺失数据;  填补方法;  方法比较
Keywords:
 ; missing data;  data imputation;  discrete data;  method comparison
分类号:
TP391.9
文献标志码:
A
摘要:
 针对离散型数据填补方法的研究尚不完备的情况，通过改造现有模型，系统地比较和分析了基于众数填补、随机填补、K最近邻填补、基于自编码器的填补和基于生成对抗网络的填补在离散型数据的填补性能，对在数据预处理阶段选择适合数据集的填补方案具有重要的意义。实验结果显示，不同填补方法的填补结果有较大的差异，进而影响后续分析的准确性。
Abstract:
 In the process of data mining, the widespread problem of missing data would largely affect the data quality and the robustness of analyses, and ultimately lead to biased decision-making. The commonly used imputation methods are mainly targeted at continuous data, most of which are not suitable for discrete data. However, there still lacks comprehensive research on discrete data imputation methods both at home and abroad. To this end, we have systematically estimated the discrete data imputation performance of several methods, including mode-based filling, random filling, K nearest neighbor filling, auto-encoder-based filling, and generative confrontation based filling, by modifying the existing models to fit discrete data. The results indicated that the performances varied largely among different filling methods, which in turn affects the accuracy of subsequent analyses. Therefore, it is crucial to choose suitable imputation scheme according to different data sets during the data preprocessing stage.

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备注/Memo

备注/Memo:
 ［收稿日期］ 2020-06-06
［基金项目］ 福建省大数据管理新技术与知识工程重点实验室开放基金（BD201801）
［第一作者］ 袁建裕(1990-)， 男， 浙江宁海人，湖北工业大学硕士研究生，研究方向为数据挖掘
［通信作者］ 叶志伟(1978-)， 男， 湖北浠水人，工学博士，湖北工业大学教授，研究方向为机器学习和数据挖掘
更新日期/Last Update: 2021-11-01