Clustering on dissimilarity representations for detecting mislabelled seismic signals at nevado del ruiz volcano
Classification of seismic signals at Colombian volcanoes has been carried out manually by visual inspection. In order to reduce the workload for the seismic analysts and to turn classification reliableand objective, the use of supervised learning algorithms has been explored; particularly classifier...
- Autores:
-
Alzate, Mauricio Orozco
Castellanos-Domínguez, César Germán
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2007
- Institución:
- Universidad Nacional de Colombia
- Repositorio:
- Universidad Nacional de Colombia
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.unal.edu.co:unal/34011
- Acceso en línea:
- https://repositorio.unal.edu.co/handle/unal/34011
http://bdigital.unal.edu.co/24091/
- Palabra clave:
- Rights
- openAccess
- License
- Atribución-NoComercial 4.0 Internacional
| Summary: | Classification of seismic signals at Colombian volcanoes has been carried out manually by visual inspection. In order to reduce the workload for the seismic analysts and to turn classification reliableand objective, the use of supervised learning algorithms has been explored; particularly classifiers built in dissimilarity spaces. Nonetheless, the performance of such learning methods is subject to the availability of a representative and a priori well classified training sets. To detect mislabeled events, the use of clustering techniques on the dissimilarity representations is proposed. Our experiments,performed on re-analyzed seismic signals, show a significant improvement respect to recognition accuracies for the original data sets. |
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