dc.creator | Segarra Martín, C. | es |
dc.creator | Martínez Ballesteros, María del Mar | es |
dc.creator | Troncoso Lora, Alicia | es |
dc.creator | Martínez Álvarez, Francisco | es |
dc.date.accessioned | 2024-04-11T07:06:07Z | |
dc.date.available | 2024-04-11T07:06:07Z | |
dc.date.issued | 2022-04 | |
dc.identifier.citation | Segarra Martín, C., Martínez Ballesteros, M.d.M., Troncoso Lora, A. y Martínez Álvarez, F. (2022). A novel approach to discover numerical association based on the Coronavirus Optimization Algorithm. En 37th ACM/SIGAPP Symposium on Applied Computing (SAC '22) (1148-1151), Association for Computing Machinery. | |
dc.identifier.isbn | 978-1-4503-8713-2 | es |
dc.identifier.uri | https://hdl.handle.net/11441/156768 | |
dc.description.abstract | The disease caused by the SARS-CoV-2 (COVID-19) has affected millions of people around the world since its detection in 2019. This pandemic inspired the development of the Coronavirus Optimization Algorithm (CVOA), a bio-inspired metaheuristic that was originally used to adjust deep learning models for time series forecasting, by means of a binary codification. In this paper, a integer codification for the CVOA individual is introduced and used for optimizing a novel approach for numerical association rules mining. As an application case, the prediction of earthquakes of large magnitude has been addressed. This kind of events are rare and, therefore, they can be characterized by rules with very high interest or lift and low support. Thus, the algorithm has been applied to the extraction of rules meeting specific criteria in an earthquake data set, provided by the National Geographic Institute of Spain. The results show CVOA as a promising tool for numerical association rules mining, obtaining rules with useful and meaningful information for predicting the occurrence of large earthquakes. | es |
dc.description.sponsorship | Ministerio de Economía y Competitividad TIN2017- 88209-C2-1-R | es |
dc.description.sponsorship | Junta de Andalucía PY20-00870 | es |
dc.description.sponsorship | Junta de Andalucía UPO-1380516 | es |
dc.format | application/pdf | es |
dc.format.extent | 4 | es |
dc.language.iso | eng | es |
dc.publisher | Association for Computing Machinery | es |
dc.relation.ispartof | 37th ACM/SIGAPP Symposium on Applied Computing (SAC '22) (2022), pp. 1148-1151. | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Bioinspired metaheuristic | es |
dc.subject | Numerical association rules | es |
dc.subject | Time series | es |
dc.subject | Earthquake magnitude prediction | es |
dc.title | A novel approach to discover numerical association based on the Coronavirus Optimization Algorithm | es |
dc.type | info:eu-repo/semantics/conferenceObject | es |
dc.type.version | info:eu-repo/semantics/publishedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos | es |
dc.relation.projectID | TIN2017- 88209-C2-1-R | es |
dc.relation.projectID | PY20-00870 | es |
dc.relation.projectID | UPO-1380516 | es |
dc.relation.publisherversion | https://dl.acm.org/doi/10.1145/3477314.3507343 | es |
dc.identifier.doi | 10.1145/3477314.3507343 | es |
dc.publication.initialPage | 1148 | es |
dc.publication.endPage | 1151 | es |
dc.eventtitle | 37th ACM/SIGAPP Symposium on Applied Computing (SAC '22) | es |
dc.relation.publicationplace | New York (Estados Unidos) | es |
dc.contributor.funder | Ministerio de Economía y Competitividad (MINECO). España | es |
dc.contributor.funder | Junta de Andalucía | es |