This paper proposes a process model for design-oriented machine learning (DS-ML) research in the area of information systems (IS). As DS-ML studies become more prevalent in addressing complex business and societal challenges, there is a need for a standardized framework to conduct, communicate, and evaluate such research. We integrate elements from the design science research (DSR) process model, action design research (ADR), and the Cross Industry Standard Process for Data Mining (CRISP-DM) to develop a comprehensive Machine Learning Process Model (MLPM) tailored for academic DS-ML studies. The MLPM outlines eight key phases, including: problem identification; objective formulation; data understanding; data preparation; design, development, and refinement; evaluation; reflection and learning; and communication. We discuss the unique aspects of each phase in the context of DS-ML research and highlight the iterative nature of the process. By providing this structured approach, we aim to enhance the rigor, transparency, and comparability of DS-ML studies in IS research. This model serves as a step towards establishing consistent standards for DS-ML research, facilitating its integration into mainstream IS literature, and unlocking new opportunities for innovation and impact in the field.

中文翻译：

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信息系统中面向设计的机器学习研究的过程模型


本文提出了一种信息系统 （IS） 领域面向设计的机器学习 （DS-ML） 研究的过程模型。随着 DS-ML 研究在解决复杂的商业和社会挑战方面变得越来越普遍，需要一个标准化的框架来开展、交流和评估此类研究。我们整合了设计科学研究 （DSR） 过程模型、行动设计研究 （ADR） 和数据挖掘跨行业标准过程 （CRISP-DM） 的元素，以开发为学术 DS-ML 研究量身定制的综合机器学习过程模型 （MLPM）。MLPM 概述了八个关键阶段，包括：问题识别;客观的表述;数据理解;数据准备;设计、开发和改进;评估;反思和学习;和沟通。我们在 DS-ML 研究的背景下讨论了每个阶段的独特方面，并强调了该过程的迭代性质。通过提供这种结构化方法，我们的目标是提高 IS 研究中 DS-ML 研究的严谨性、透明度和可比性。该模型是为 DS-ML 研究建立一致标准、促进其融入主流 IS 文献并为该领域的创新和影响释放新机会的一步。