Classifying Flies Based on Reconstructed Audio Signals

Flynn, Michael; Bagnall, Anthony

doi:10.1007/978-3-030-33617-2_26

Cited by 4 publications

(2 citation statements)

References 19 publications

(18 reference statements)

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“…The accuracy of Rocket and MiniRocket on the InsectSound and MosquitoSound datasets appears to be broadly comparable to reported results for other methods for these datasets or versions of these datasets [5,10,11,39], although some deep learning approaches are significantly more accurate on MosquitoSound [10]…”

Section: Scalabilitysupporting

confidence: 70%

MiniRocket

Dempster

Schmidt

Webb

2021

Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery &Amp; Data Mining

207

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Rocket achieves state-of-the-art accuracy for time series classification with a fraction of the computational expense of most existing methods by transforming input time series using random convolutional kernels, and using the transformed features to train a linear classifier. We reformulate Rocket into a new method, MiniRocket. MiniRocket is up to 75 times faster than Rocket on larger datasets, and almost deterministic (and optionally, fully deterministic), while maintaining essentially the same accuracy. Using this method, it is possible to train and test a classifier on all of 109 datasets from the UCR archive to state-of-the-art accuracy in under 10 minutes. MiniRocket is significantly faster than any other method of comparable accuracy (including Rocket), and significantly more accurate than any other method of remotely similar computational expense. CCS CONCEPTS• Computing methodologies → Machine learning; • Information systems → Data mining.

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Section: Scalabilitysupporting

confidence: 70%

MiniRocket

Dempster

Schmidt

Webb

2021

Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery &Amp; Data Mining

207

View full text Add to dashboard Cite

show abstract

“…The accuracy of Rocket and MiniRocket on the InsectSound and MosquitoSound datasets appears to be broadly comparable to reported results for other methods for these datasets or versions of these datasets [5,39,10,11], although some deep learning approaches are significantly more accurate on MosquitoSound [10].…”

Section: Ucr Archivesupporting

confidence: 69%

MINIROCKET: A Very Fast (Almost) Deterministic Transform for Time Series Classification

Dempster,

Schmidt,

Webb

2020

Preprint

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Until recently, the most accurate methods for time series classification were limited by high computational complexity. Rocket achieves stateof-the-art accuracy with a fraction of the computational expense of most existing methods by transforming input time series using random convolutional kernels, and using the transformed features to train a linear classifier. We reformulate Rocket into a new method, MiniRocket, making it up to 75 times faster on larger datasets, and making it almost deterministic (and optionally, with additional computational expense, fully deterministic), while maintaining essentially the same accuracy. Using this method, it is possible to train and test a classifier on all of 109 datasets from the UCR archive to state-of-the-art accuracy in less than 10 minutes. MiniRocket is significantly faster than any other method of comparable accuracy (including Rocket), and significantly more accurate than any other method of even roughly-similar computational expense. As such, we suggest that MiniRocket should now be considered and used as the default variant of Rocket.

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