The effects of data quality on machine learning performance on tabular data

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

Information Systems Pub Date : 2025-03-14 DOI:10.1016/j.is.2025.102549

Sedir Mohammed , Lukas Budach , Moritz Feuerpfeil , Nina Ihde , Andrea Nathansen , Nele Noack , Hendrik Patzlaff , Felix Naumann , Hazar Harmouch

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引用次数: 0

Abstract

Modern artificial intelligence (AI) applications require large quantities of training and test data. This need creates critical challenges not only concerning the availability of such data, but also regarding its quality. For example, incomplete, erroneous, or inappropriate training data can lead to unreliable models that produce ultimately poor decisions. Trustworthy AI applications require high-quality training and test data along many quality dimensions, such as accuracy, completeness, and consistency.

We explore empirically the relationship between six data quality dimensions and the performance of 19 popular machine learning algorithms covering the tasks of classification, regression, and clustering, with the goal of explaining their performance in terms of data quality. Our experiments distinguish three scenarios based on the AI pipeline steps that were fed with polluted data: polluted training data, test data, or both. We conclude the paper with an extensive discussion of our observations.

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数据质量对表数据机器学习性能的影响

现代人工智能（AI）应用需要大量的训练和测试数据。这一需求不仅对这种数据的可得性，而且对其质量都构成了严峻的挑战。例如，不完整、错误或不适当的训练数据可能导致不可靠的模型，最终产生糟糕的决策。值得信赖的人工智能应用程序需要高质量的训练和测试数据，以及许多质量维度，如准确性、完整性和一致性。我们从经验上探讨了六个数据质量维度与19种流行的机器学习算法的性能之间的关系，这些算法涵盖了分类、回归和聚类等任务，目的是解释它们在数据质量方面的性能。我们的实验根据人工智能管道步骤区分了三种场景，这些步骤被污染的数据提供：污染的训练数据，测试数据，或两者兼而有之。最后，我们对我们的观察结果进行了广泛的讨论。

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.