Adaptive classification using incremental learning for seismic-volcanic signals with concept drift

The accurate labeling of the volcanic earthquake signals is a crucial task in order to estimate the increase in volcanic activity (among other parameters), which contributes to determine a state of volcanic unrest (as a possible precursor of an eruption). Several automatic classification approaches have been proposed in different computer science areas in order to complement the exhaustive human task of manually labeling volcano-seismic events. However, most of these approaches have been designed under the assumption of stationarity; that is, by discarding the changing nature of the volcanic phenomenon that evolves over time. In this work, an adaptive classification strategy based on incremental learning is proposed, which identifies and learns concept drifts of data streams coming from seismic recordings. The proposed strategy uses a classifier ensemble in order to handle recurrent states and classifies data even while true labels are not yet available. Unlike usual experimental protocols in the state–of–the–art, we carried out experiments with seismic data from Villarrica volcano, considering the chronological order of the data during a long period (more than 4 years of registration) to achieve the detection of drifts. The results show that the proposed classification strategy satisfactorily counteracts the changes, in contrast to standard classifiers trained within a traditional learning scheme. The proposed strategy keeps a stable classification accuracy and achieves a reduction of up to 0.28 of the absolute error in episodes of abrupt changes.