A review on missing values for main challenges and methods

IF 3 2区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Systems Pub Date : 2023-10-01 DOI:10.1016/j.is.2023.102268

Lijuan Ren , Tao Wang , Aicha Sekhari Seklouli , Haiqing Zhang , Abdelaziz Bouras

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引用次数: 0

Abstract

Several recent reviews summarize common missing value analysis methods. However, none of them provide a systematic and in-depth summary of the analytical challenges and solutions for dealing with missing values. For the purpose of guiding the handling of missing values, this review aims to consolidate current developments in novel missing-value research methodologies. In particular, we comprehensively investigated cutting-edge missing value solutions and methodically studied the main challenges associated with missing values analysis (missing mechanisms, missing patterns, and missing rates). Furthermore, we reviewed 63 publications that compare different strategies for deleting and imputing missing values. Then we investigated data characteristics, highlighted three main problems when analyzing missing values, and analyzed the performance of missing value solutions in these studied papers. Moreover, we conducted comprehensive experiments on 9 public datasets using typical missing value processing methods and provided a simple guided decision tree for handling missing values. Finally, we described current Research hotspots and open challenges, which give potential research topics.

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主要挑战和方法的缺失值综述

最近的几篇综述总结了常见的缺失值分析方法。然而，他们都没有提供一个系统的和深入的总结分析挑战和解决方案，以处理缺失的价值。为了指导缺失值的处理，本综述旨在整合当前新的缺失值研究方法的发展。特别是，我们全面研究了前沿的缺失价值解决方案，并系统地研究了与缺失价值分析相关的主要挑战(缺失机制、缺失模式和缺失率)。此外，我们回顾了63篇比较删除和输入缺失值的不同策略的出版物。然后，我们研究了数据特征，突出了缺失值分析中存在的三个主要问题，并分析了这些研究论文中缺失值解的性能。此外，我们在9个公共数据集上使用典型的缺失值处理方法进行了综合实验，并提供了一种简单的指导决策树来处理缺失值。最后，我们描述了当前的研究热点和开放的挑战，提出了潜在的研究课题。

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

Information Systems 工程技术-计算机：信息系统

CiteScore

9.40

自引率

2.70%

发文量

112

审稿时长

53 days

期刊介绍： Information systems are the software and hardware systems that support data-intensive applications. The journal Information Systems publishes articles concerning the design and implementation of languages, data models, process models, algorithms, software and hardware for information systems. Subject areas include data management issues as presented in the principal international database conferences (e.g., ACM SIGMOD/PODS, VLDB, ICDE and ICDT/EDBT) as well as data-related issues from the fields of data mining/machine learning, information retrieval coordinated with structured data, internet and cloud data management, business process management, web semantics, visual and audio information systems, scientific computing, and data science. Implementation papers having to do with massively parallel data management, fault tolerance in practice, and special purpose hardware for data-intensive systems are also welcome. Manuscripts from application domains, such as urban informatics, social and natural science, and Internet of Things, are also welcome. All papers should highlight innovative solutions to data management problems such as new data models, performance enhancements, and show how those innovations contribute to the goals of the application.