Towards a Problem-Oriented Domain Adaptation Framework for Machine Learning

Abstract

Domain adaptation is a sub-field of machine learning that involves transferring knowledge from a source domain to perform the same task in a target domain. This challenge commonly arises when data is obtained from multiple sources or when working with datasets that evolve over time. While recent advances offer promising methods, researchers and practitioners still struggle to determine whether domain adaptation is suitable for a given problem—and subsequently, which approach to select. This article develops a problem-oriented framework for domain adaptation through a systematic, iterative development and evaluation methodology, refined through three evaluation episodes. The framework distinguishes five domain adaptation scenarios, provides tailored recommendations for addressing each scenario, and offers practical guidelines for identifying the appropriate scenario for a given problem. Through multiple evaluation episodes, we tested the framework on both artificial and real-world datasets, as well as through an experimental study with 100 participants. The evaluation demonstrates that the framework correctly categorized all three real-world problem instances examined and significantly improved practitioners’ diagnostic accuracy in the controlled experiment. In summary, we provide clear, actionable guidance for researchers and practitioners seeking to employ domain adaptation techniques, even without specialized domain adaptation expertise.