Data stream prediction is challenging when concepts drift, processing time, and memory
constraints come into account. Concept drift refers to changes in data distribution over time …

Data stream applications usually suffer from multiple types of concept drift. However, most
existing approaches are only able to handle a subset of types of drift well, hindering …

In many real-world scenarios, we often deal with streaming data that is sequentially
collected over time. Due to the non-stationary nature of the environment, the streaming data …

In a data stream, concept drift refers to unpredictable distribution changes over time, which
violates the identical-distribution assumption required by conventional machine learning …

Abstract In the “Big Data” age, the amount and distribution of data have increased wildly and
changed over time in various time-series-based tasks, eg weather prediction, network …

In concept drift adaptation, we aim to design a blind or an informed strategy to update our
best predictor for future data at each time point. However, existing informed drift adaptation …

As an excellent ensemble algorithm, Gradient Boosting Decision Tree (GBDT) has been
tested extensively with static data. However, real-world applications often involve dynamic …

Data streams may encounter data distribution changes, which can significantly impair the
accuracy of models. Concept drift detection tracks data distribution changes and signals …

Concept drift refers to changes in the distribution of underlying data and is an inherent
property of evolving data streams. Ensemble learning, with dynamic classifiers, has proved …

In many real-world applications, data are often collected in the form of a stream, and thus the
distribution usually changes in nature, which is referred to as concept drift in the literature …