On the Usage and Performance of the Hierarchical Vote Collective of Transformation-Based Ensembles Version 1.0 (HIVE-COTE v1.0)

Bagnall, Tony, Flynn, Michael, Large, James and Middlehurst, Matthew (2020) On the Usage and Performance of the Hierarchical Vote Collective of Transformation-Based Ensembles Version 1.0 (HIVE-COTE v1.0). In: Lecture Notes in Computer Science. Springer, pp. 3-18. ISBN 978-3-030-65741-3

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Abstract

The Hierarchical Vote Collective of Transformation-based Ensembles (HIVE-COTE) is a heterogeneous meta ensemble for time series classification. Since it was first proposed in 2016, the algorithm has undergone some minor changes and there is now a configurable, scalable and easy to use version available in two open source repositories. We present an overview of the latest stable HIVE-COTE, version 1.0, and describe how it differs to the original. We provide a walkthrough guide of how to use the classifier, and conduct extensive experimental evaluation of its predictive performance and resource usage. We compare the performance of HIVE-COTE to three recently proposed algorithms.

Item Type:	Book Section
Uncontrolled Keywords:	classification,hive-cote,heterogeneous ensembles,time series,theoretical computer science,computer science(all) ,/dk/atira/pure/subjectarea/asjc/2600/2614
Faculty \ School:	Faculty of Science > School of Computing Sciences
UEA Research Groups:	Faculty of Science > Research Groups > Data Science and AI
Related URLs:	http://www.scopus.com/inward/record.url?...
Depositing User:	LivePure Connector
Date Deposited:	05 Jan 2021 00:34
Last Modified:	28 Mar 2025 02:10
URI:	https://ueaeprints.uea.ac.uk/id/eprint/78015
DOI:	10.1007/978-3-030-65742-0_1

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