Hodge, Victoria J. orcid.org/0000-0002-2469-0224 and Austin, James orcid.org/0000-0001-5762-8614 (2018) An Evaluation of Classification and Outlier Detection Algorithms. [Preprint]
Abstract
This paper evaluates algorithms for classification and outlier detection accuracies in temporal data. We focus on algorithms that train and classify rapidly and can be used for systems that need to incorporate new data regularly. Hence, we compare the accuracy of six fast algorithms using a range of well-known time-series datasets. The analyses demonstrate that the choice of algorithm is task and data specific but that we can derive heuristics for choosing. Gradient Boosting Machines are generally best for classification but there is no single winner for outlier detection though Gradient Boosting Machines (again) and Random Forest are better. Hence, we recommend running evaluations of a number of algorithms using our heuristics.
Metadata
Item Type: | Preprint |
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Authors/Creators: |
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Keywords: | Anomaly Detection,Classification,Algorithms,outlier detection,TIME-SERIES |
Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Computer Science (York) |
Depositing User: | Pure (York) |
Date Deposited: | 08 Jun 2023 23:14 |
Last Modified: | 02 Apr 2025 23:30 |
Status: | Published |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:200174 |