A Semi-Supervised Approach for Microseisms Classification from Cotopaxi Volcano

Carlos Brusil, Felipe Grijalva, Roman Lara-Cueva, Mario Ruiz, Byron Acuna

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

8 Citas (Scopus)

Resumen

Microseism classification is primordial to easily identify what type of event we are facing at in a possibly dangerous situation. However, labeling events is a hard and time-consuming task since it requires expert volcanologists to do this work. To alleviate the need for abundant labeled data, we propose a semi-supervised approach using the self-training algorithm. First, we extract several relevant microseisms features from the registers on the provided database, then we apply PCA to reduce redundancy on the features and finally we classify them using an SVM classifier. As a result of this methodology we show that although the accuracy of using a supervised scheme is still better than a semi-supervised one, if we allow a 10% of false positive rate, our approach achieves similar performance to supervised techniques with only 50% of labeled data. This demonstrates the potential of semi-supervised schemes.

Idioma originalInglés
Título de la publicación alojada2019 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2019
EditorialInstitute of Electrical and Electronics Engineers Inc.
ISBN (versión digital)9781728156668
DOI
EstadoPublicada - nov. 2019
Publicado de forma externa
Evento6th IEEE Latin American Conference on Computational Intelligence, LA-CCI 2019 - Guayaquil, Ecuador
Duración: 11 nov. 201915 nov. 2019

Serie de la publicación

Nombre2019 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2019

Conferencia

Conferencia6th IEEE Latin American Conference on Computational Intelligence, LA-CCI 2019
País/TerritorioEcuador
CiudadGuayaquil
Período11/11/1915/11/19

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