A tale of two toolkits, report the third: on the usage and performance of HIVE-COTE v1.0
Bagnall, Anthony, Flynn, Michael, Large, James, Lines, Jason, Middlehurst, Matthew
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.
Apr-25-2020
- Country:
- Europe > United Kingdom > England (0.04)
- Genre:
- Research Report > Experimental Study (0.46)
- Technology:
- Information Technology
- Artificial Intelligence > Machine Learning (1.00)
- Data Science (1.00)
- Information Management (0.68)
- Information Technology