- author = "Ziyu Qiu and Malcolm I. Heywood",
- title = "Benchmarking Streaming Evolutionary Ensemble Learning under Shifting Imbalanced Data",
- booktitle = "2025 IEEE Congress on Evolutionary Computation (CEC)",
- year = "2025",
- editor = "Yaochu Jin and Thomas Baeck",
- address = "Hangzhou, China",
- month = "8-12 " # jun,
- publisher = "IEEE",
- keywords = "genetic algorithms, genetic programming, Accuracy, Buildings, Forestry, Evolutionary computation, Benchmark testing, Data models, Windows, Classification algorithms, Ensemble learning, Context modeling",
- isbn13 = "979-8-3315-3432-5",
-
DOI = "
10.1109/CEC65147.2025.11043023",
- abstract = "Streaming classification tasks with shifting imbalanced data distributions imply that the process creating the data are non-stationary. Such a streaming context is particularly challenging because model building, at any point in time, can only be performed relative to a small (incomplete) sample of the data. We demonstrate that both crossover and ensemble learning are particularly important for competitive performance under two challenging benchmarks. One benchmark represents a Botnet detection task and the second reformulates the Forest cover type classification task for shift (as opposed to drift). Comparison with the contemporary Learn++.NSE streaming classifier indicates specific advantages and disadvantages as viewed from the perspective of null-bias and prequential performance metrics.",
-
notes = "also known as \cite{11043023}
",
