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.

An emergency event is a sudden, urgent, usually unexpected incident or occurrence that requires an immediate reaction or assistance for emergency situations, which plays an increasingly important role in the global economy and in our daily lives. Recently, the web is becoming an important event information provider and repository due to its real-time, open, and dynamic features. In this paper, web resources based states detecting algorithm of an event is developed in order to let the people know of an emergency event clearly and help the social group or government process the emergency events effectively. The relationship between web and emergency events is first introduced, which is the foundation of using web resources to detect the state of emergency events imaged on the web. Second, five temporal features of emergency events are developed to provide the basis for state detection. Moreover, the outbreak power and the fluctuation power are presented to integrate the above temporal features for measuring the different states of an emergency event. Using these two powers, an automatic state detecting algorithm for emergency events is proposed. In addition, heuristic rules for detecting the states of emergency event on the web are discussed. Our evaluations using real-world data sets demonstrate the utility of the proposed algorithm, in terms of performance and effectiveness in the analysis of emergency events.