Concept drift is a phenomenon where the distribution of data streams changes over time. When this happens, model predictions become less accurate. Hence, models built in the past need to be re-learned for the current data. Two design questions need to be addressed in designing a strategy to re-learn models: which type of concept drift has occurred, and how to utilize the drift type to improve re-learning performance. Existing drift detection methods are often good at determining when drift has occurred. However, few retrieve information about how the drift came to be present in the stream. Hence, determining the impact of the type of drift on adaptation is difficult. Filling this gap, we designed a framework based on a lazy strategy called Type-Driven Lazy Drift Adaptor (Type-LDA). Type-LDA first retrieves information about both how and when a drift has occurred, then it uses this information to re-learn the new model. To identify the type of drift, a drift type identifier is pre-trained on synthetic data of known drift types. Furthermore, a drift point locator locates the optimal point of drift via a sharing loss. Hence, Type-LDA can select the optimal point, according to the drift type, to re-learn the new model. Experiments validate Type-LDA on both synthetic data and real-world data, and the results show that accurately identifying drift type can improve adaptation accuracy.

Concept drift is a phenomenon that the distribution of data streams changes with time. When this happens, model predictions become less accurate. Hence, concept drift needs to be detected and adapted. Existing drift detection methods are good at determining when drift has occurred, but few retrieve information about how the drift came to be present in the stream, i.e., what type of drift has occurred. Hence, discussing the impact of the type of drift on adaptation is a difficult thing. To fill this gap, we propose a pre-trained framework for training a drift detector called a type-driven lite concept drift detector (Type-LDD) that retrieves information about both when and how a drift has occurred. In our proposed pre-trained framework, the Type-LDD including a drift-type identifier and a drift-point locator was based on a synthetic dataset containing a range of drift types. When repurposing the pre-trained model for detecting new data streams, a knowledge distillation module fine-tunes the proposed Type-LDD to speed up inference and keep detection accuracy. The proposed Type-LDD is validated on both synthetic data and real-world data, and demonstrated that accurately identifying the type of drift that has occurred can improve adaptation accuracy.