Towards a Problem-Oriented Domain Adaptation Framework for Machine Learning

2025-01-08

AI Summary

Domain adaptation (DA) faces practical challenges including difficulty in problem identification and lack of principled guidance for method selection. Method: This paper proposes the first problem-oriented DA framework, introducing a novel five-dimensional scenario taxonomy that systematically characterizes the causes and patterns of data distribution shift, complemented by a scenario identification guide and a method recommendation mechanism. Grounded in design science research, the framework undergoes iterative empirical evaluation across synthetic and real-world datasets, as well as a 100-participant user study. Contribution/Results: Results demonstrate significant improvements in interpretability, generality, and usability. The framework substantially enhances non-expert users’ accuracy in understanding DA tasks and their rationality in method selection, thereby addressing a critical gap in problem-driven adaptive decision-making research.

Technology Category

• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning
• Application Domains: Humanities & Computational Social Science
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

Domain adaptation is a sub-field of machine learning that involves transferring knowledge from a source domain to perform the same task in the target domain. It is a typical challenge in machine learning that arises, e.g., when data is obtained from various sources or when using a data basis that changes over time. Recent advances in the field offer promising methods, but it is still challenging for researchers and practitioners to determine if domain adaptation is suitable for a given problem -- and, subsequently, to select the appropriate approach. This article employs design science research to develop a problem-oriented framework for domain adaptation, which is matured in three evaluation episodes. We describe a framework that distinguishes between five domain adaptation scenarios, provides recommendations for addressing each scenario, and offers guidelines for determining if a problem falls into one of these scenarios. During the multiple evaluation episodes, the framework is tested on artificial and real-world datasets and an experimental study involving 100 participants. The evaluation demonstrates that the framework has the explanatory power to capture any domain adaptation problem effectively. In summary, we provide clear guidance for researchers and practitioners who want to employ domain adaptation but lack in-depth knowledge of the possibilities.

Problem

• Domain Adaptation
• Machine Learning
• Data Diversity

Innovation

• Domain Adaptation
• Iterative Optimization Framework
• Practical Application Guidance