A Survey of Domain Knowledge Elicitation in Applied Machine Learning
A Survey of Domain Knowledge Elicitation in Applied Machine Learning
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DOI：
10.3390/mti5120073
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发表时间：
2021-12-01
影响因子：
2.5
通讯作者：
Bertini, Enrico
中科院分区：
文献类型：
--
作者：
Kerrigan, Daniel;Hullman, Jessica;Bertini, Enrico
从领域专家那里获取知识可以在整个机器学习过程中发挥重要作用，从正确指定任务到评估模型结果。然而，知识获取也充满了挑战。在这项工作中，我们考虑了机器学习研究人员为什么以及如何在模型开发过程中从专家那里获取知识。我们开发了一个分类系统，根据启发的目标，启发目标，启发过程，并使用引发的知识来表征启发方法。我们分析了28篇论文中观察到的启发趋势，并确定了增加这些启发方法的严谨性的机会。我们提出了未来的研究方向，在启发式机器学习，通过强调进一步探索的途径，并借鉴我们可以从其他领域的启发式研究。
Eliciting knowledge from domain experts can play an important role throughout the machine learning process, from correctly specifying the task to evaluating model results. However, knowledge elicitation is also fraught with challenges. In this work, we consider why and how machine learning researchers elicit knowledge from experts in the model development process. We develop a taxonomy to characterize elicitation approaches according to the elicitation goal, elicitation target, elicitation process, and use of elicited knowledge. We analyze the elicitation trends observed in 28 papers with this taxonomy and identify opportunities for adding rigor to these elicitation approaches. We suggest future directions for research in elicitation for machine learning by highlighting avenues for further exploration and drawing on what we can learn from elicitation research in other fields.