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arXiv Machine Learning By Cian Higgins, Gerard Carrigan, Pinar Sungu Isiacik, Georgiana Ifrim

Revisiting WEASEL 2.0: Reproduction, Sensitivity, and an Adaptive Ensemble-Size Rule

Read the original on arXiv Machine Learning →

WEASEL 2.0 is a dictionary‑based time‑series classifier that uses dilated sliding windows, a randomised hyperparameter ensemble, and a fixed‑size dense feature representation. The authors reproduced the method on 114 UCR datasets, matching the original accuracy results, and examined the sensitivity of four design choices. They found that the rule for maximum ensemble size is over‑provisioned for long series and proposed an adaptive rule that reduces memory and time usage without significantly affecting accuracy.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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