Abstract
The volcanic activity analysis by means of seismic signals is a scenario typically treated by studies in the Artificial Intelligence area under the assumption of invariant probability distribution over time. The literature in geophysics, on the other hand, qualitatively claims that the volcanic phenomenon evolves over long periods of time. This article shows, by three methods, one supervised and two unsupervised, the existence of significant changes in the intrinsic components of the data (concept drifts) generated within the volcanic phenomenon. Here it is also shown how the performance of a learning model is considerably affected in a classification task, when concept drifts are not treated in the analysis of a volcanic environment.
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Acknowledgment
This work is supported by Programa Nacional de Fomento a la Formación de Investigadores, Doctorados Nacionales − COLCIENCIAS. The authors would like to thank OVDAS and UFRO staff for their contributions.
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Castro-Cabrera, P.A., Orozco-Alzate, M., Castellanos-Domínguez, C.G., Huenupán, F., Franco, L.E. (2018). Supervised and Unsupervised Identification of Concept Drifts in Data Streams of Seismic-Volcanic Signals. In: Simari, G., Fermé, E., Gutiérrez Segura, F., Rodríguez Melquiades, J. (eds) Advances in Artificial Intelligence - IBERAMIA 2018. IBERAMIA 2018. Lecture Notes in Computer Science(), vol 11238. Springer, Cham. https://doi.org/10.1007/978-3-030-03928-8_16
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