D2MR: 머신러닝 시스템에서 데이터 특성 기반의 규칙 매핑을 통한 드리프트 탐지 기법 추천 프레임워크 = D2MR: A Framework for Recommending Drift Detection Method via Data Characteristic-based Rule Mapping in Machine Learning System

다국어 초록 (Multilingual Abstract)

Machine learning systems often suffer from performance degradation and reduced model reliability due to data distribution shifts (drift) that occur over time. Although various drift detection methods have been studied to address this issue, selecting a method that is well-suited to the characteristics of real-world datasets remains challenging. This study proposes the D2MR(Drift Detection Method Recommender) framework, which automatically recommends suitable detection methods based on dataset meta-features such as label availability, data type, size, dimensionality, distance computability, and distribution type. The proposed framework systematically maps data characteristics to detection methods and can be effectively integrated into drift monitoring modules of machine learning systems. Finally, the effectiveness of D2MR is validated through case studies using the CIFAR-10 image dataset and the UCI Wine Quality dataset.