Publikationsdatum:
2023-01-27
Beschreibung:
In practice, it is very demanding and sometimes impossible to collect
datasets of tagged data large enough to successfully train a machine learning
model, and one possible solution to this problem is transfer learning. This
study aims to assess how transferable are the features between different
domains of time series data and under which conditions. The effects of transfer
learning are observed in terms of predictive performance of the models and
their convergence rate during training. In our experiment, we use reduced data
sets of 1,500 and 9,000 data instances to mimic real world conditions. Using
the same scaled-down datasets, we trained two sets of machine learning models:
those that were trained with transfer learning and those that were trained from
scratch. Four machine learning models were used for the experiment. Transfer of
knowledge was performed within the same domain of application (seismology), as
well as between mutually different domains of application (seismology, speech,
medicine, finance). We observe the predictive performance of the models and the
convergence rate during the training. In order to confirm the validity of the
obtained results, we repeated the experiments seven times and applied
statistical tests to confirm the significance of the results. The general
conclusion of our study is that transfer learning is very likely to either
increase or not negatively affect the predictive performance of the model or
its convergence rate. The collected data is analysed in more details to
determine which source and target domains are compatible for transfer of
knowledge. We also analyse the effect of target dataset size and the selection
of model and its hyperparameters on the effects of transfer learning.
Beschreibung:
Croatian Science Foundation projects UIP-2019-04-7999, DOK-2020-01-4659 and IP-2020-02-3770,
COST project G2Net CA17137 A network for Gravitational Waves, Geophysics and Machine Learning. Partially supported by the project INGV Pianeta Dinamico 2021 Tema 8 SOME (CUP D53J1900017001) funded by Italian Ministry of University and Research “Fondo finalizzato al rilancio degli investimenti delle amministrazioni centrali dello Stato
Beschreibung:
Published
Beschreibung:
107976
Beschreibung:
8T. Sismologia in tempo reale e Early Warning Sismico e da Tsunami
Beschreibung:
JCR Journal
Schlagwort(e):
machine learning, transfer learning, time series, fine-tuning, convolutional neural networks
Repository-Name:
Istituto Nazionale di Geofisica e Vulcanologia (INGV)
Materialart:
article
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