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Early classification of spatio-temporal events using partial information
This paper investigates event extraction and early event classification in contiguous spatio-temporal data streams, where events need to be classified using partial information, i.e. while the event is ongoing. The framework incorporates an event extraction algorithm and an early event classificatio...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7406362/ https://www.ncbi.nlm.nih.gov/pubmed/32756613 http://dx.doi.org/10.1371/journal.pone.0236331 |
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author | Kandanaarachchi, Sevvandi Hyndman, Rob J. Smith-Miles, Kate |
author_facet | Kandanaarachchi, Sevvandi Hyndman, Rob J. Smith-Miles, Kate |
author_sort | Kandanaarachchi, Sevvandi |
collection | PubMed |
description | This paper investigates event extraction and early event classification in contiguous spatio-temporal data streams, where events need to be classified using partial information, i.e. while the event is ongoing. The framework incorporates an event extraction algorithm and an early event classification algorithm. We apply this framework to synthetic and real problems and demonstrate its reliability and broad applicability. The algorithms and data are available in the R package eventstream, and other code in the supplementary material. |
format | Online Article Text |
id | pubmed-7406362 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-74063622020-08-13 Early classification of spatio-temporal events using partial information Kandanaarachchi, Sevvandi Hyndman, Rob J. Smith-Miles, Kate PLoS One Research Article This paper investigates event extraction and early event classification in contiguous spatio-temporal data streams, where events need to be classified using partial information, i.e. while the event is ongoing. The framework incorporates an event extraction algorithm and an early event classification algorithm. We apply this framework to synthetic and real problems and demonstrate its reliability and broad applicability. The algorithms and data are available in the R package eventstream, and other code in the supplementary material. Public Library of Science 2020-08-05 /pmc/articles/PMC7406362/ /pubmed/32756613 http://dx.doi.org/10.1371/journal.pone.0236331 Text en © 2020 Kandanaarachchi et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Kandanaarachchi, Sevvandi Hyndman, Rob J. Smith-Miles, Kate Early classification of spatio-temporal events using partial information |
title | Early classification of spatio-temporal events using partial information |
title_full | Early classification of spatio-temporal events using partial information |
title_fullStr | Early classification of spatio-temporal events using partial information |
title_full_unstemmed | Early classification of spatio-temporal events using partial information |
title_short | Early classification of spatio-temporal events using partial information |
title_sort | early classification of spatio-temporal events using partial information |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7406362/ https://www.ncbi.nlm.nih.gov/pubmed/32756613 http://dx.doi.org/10.1371/journal.pone.0236331 |
work_keys_str_mv | AT kandanaarachchisevvandi earlyclassificationofspatiotemporaleventsusingpartialinformation AT hyndmanrobj earlyclassificationofspatiotemporaleventsusingpartialinformation AT smithmileskate earlyclassificationofspatiotemporaleventsusingpartialinformation |