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首页> 外文期刊>Communications of the Association for Information Systems >How to Conduct Rigorous Supervised Machine Learning in Information Systems Research: The Supervised Machine Learning Report Card
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How to Conduct Rigorous Supervised Machine Learning in Information Systems Research: The Supervised Machine Learning Report Card

机译：如何在信息系统研究中进行严格的监督机器学习：监督机器学习报告卡

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摘要

In the last decade, applying supervised machine learning (SML) has become increasingly popular in the information systems (IS) field. However, SML results rely on many different data-preprocessing techniques, algorithms, and ways to implement them, which has contributed to an inconsistency in the way researchers have documented their SML efforts and, thus, the degree to which others can reproduce their results. In one sense, we can understand this inconsistency given the goals and motivations for SML applications vary and the research area’s rapid evolution. However, for the IS research community, the inconsistency poses a big challenge because, even with full access to the data, researchers can neither completely evaluate the SML approaches that previous research has adopted or replicate previous research results. Therefore, in this paper, we provide the IS community with guidelines for comprehensively and rigorously conducting and documenting SML research. First, we review the literature concerning steps and SML process frameworks to extract relevant problem characteristics that researchers should report and relevant choices that they should make in applying SML. Second, we integrate these characteristics and choices into a comprehensive “Supervised Machine Learning Report Card (SMLR)” that researchers can use in future SML endeavors. Third, we apply this report card to a set of 121 relevant papers published in renowned IS outlets between 2010 and 2018 and demonstrate how and where these papers’ authors could have improved their documentation and, thus, how and where researchers can better document their SML approaches in the future. Thus, with this work, we help researchers more completely and rigorously apply and document SML approaches and, thereby, enable researchers to more deeply evaluate and reproduce/replicate results in the IS field.
机译：在过去的十年中，在信息系统（IS）字段中，应用监督机器学习（SML）越来越受欢迎。然而，SML结果依赖于许多不同的数据预处理技术，算法和实现它们的方法，这导致研究人员记录了他们的SML努力的方式不一致，因此，其他人可以重现它们的结果。有一种意义，我们可以了解这种不一致，因为SML应用程序的目标和动机不同，以及研究区的快速进化。然而，对于属于研究社区来说，不一致的挑战是一个很大的挑战，因为即使充分访问了数据，研究人员也可以完全评估以前研究采用或复制以前的研究结果的SML方法。因此，在本文中，我们为社区提供了全面和严格进行和记录SML研究的指导方针。首先，我们审查了关于步骤和SML进程框架的文献，以提取研究人员应该报告和相关选择，以便在应用SML时进行报告和相关的选择。其次，我们将这些特点和选择整合到一个全面的“监督机器学习报告卡（SMLR）”，该研究人员可以在未来的SML努力中使用。第三，我们将此报告卡应用于2010年和2018年之间的名录的一组121篇相关论文，这是2010年和2018年之间的出口，并展示了这些论文的作者可以改善他们的文件以及研究人员如何以及在哪些研究人员如何更好地记录他们的SML未来的方法。因此，通过这项工作，我们帮助研究人员更完全且严格地申请和记录SML方法，从而使研究人员能够更深入地评估和重现/复制的结果。

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