Fingerprinting Concepts in Data Streams with Supervised and Unsupervised Meta-Information. - Ben Halstead; Yun Sing Koh; Patricia Riddle; Mykola Pechenizkiy; Albert Bifet; Russel Pears (ICDE, 2021)

Recurring concept memory management in data streams: exploiting data stream concept evolution to improve performance and transparency. - Ben Halstead; Yun Sing Koh; Patricia Riddle; Russel Pears; Mykola Pechenizkiy; Albert Bifet (Data Min. Knowl. Discov., 2021)

Active Learning Using Difficult Instances. - Bowen Chen 003; Yun Sing Koh; Ben Halstead (AI, 2022)

Measuring Difficulty of Learning Using Ensemble Methods. - Bowen Chen 003; Yun Sing Koh; Ben Halstead (AusDM, 2022)

A Probabilistic Framework for Adapting to Changing and Recurring Concepts in Data Streams. - Ben Halstead; Yun Sing Koh; Patricia Riddle; Mykola Pechenizkiy; Albert Bifet (DSAA, 2022)

Analyzing and repairing concept drift adaptation in data stream classification. - Ben Halstead; Yun Sing Koh; Patricia Riddle; Russel Pears; Mykola Pechenizkiy; Albert Bifet; Gustavo Olivares; Guy Coulson (Mach. Learn., 2022)

FALL: A Modular Adaptive Learning Platform for Streaming Data. - Ben Halstead; Yun Sing Koh; Patricia Riddle; Mykola Pechenizkiy; Albert Bifet (ICDE, 2023)

Combining Diverse Meta-Features to Accurately Identify Recurring Concept Drift in Data Streams. - Ben Halstead; Yun Sing Koh; Patricia Riddle; Mykola Pechenizkiy; Albert Bifet (ACM Trans. Knowl. Discov. Data, 2023)