Data streams classification using deep learning under different speeds and drifts

Autor: Lara Benítez, Pedro, Carranza García, Manuel, Gutiérrez Avilés, David, Riquelme Santos, José Cristóbal
Přispěvatelé: Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos, Ministerio de Ciencia, Innovación y Universidades (MICINN). España, Junta de Andalucía
Rok vydání: 2022
Předmět:
Zdroj: idUS. Depósito de Investigación de la Universidad de Sevilla
instname
ISSN: 1368-9894
1367-0751
DOI: 10.1093/jigpal/jzac033
Popis: Processing data streams arriving at high speed requires the development of models that can provide fast and accurate predictions. Although deep neural networks are the state-of-the-art for many machine learning tasks, their performance in real-time data streaming scenarios is a research area that has not yet been fully addressed. Nevertheless, much effort has been put into the adaption of complex deep learning (DL) models to streaming tasks by reducing the processing time. The design of the asynchronous dual-pipeline DL framework allows making predictions of incoming instances and updating the model simultaneously, using two separate layers. The aim of this work is to assess the performance of different types of DL architectures for data streaming classification using this framework. We evaluate models such as multi-layer perceptrons, recurrent, convolutional and temporal convolutional neural networks over several time series datasets that are simulated as streams at different speeds. In addition, we evaluate how the different architectures react to concept drifts typically found in evolving data streams. The obtained results indicate that convolutional architectures achieve a higher performance in terms of accuracy and efficiency, but are also the most sensitive to concept drifts. Ministerio de Ciencia, Innovación y Universidades PID2020-117954RB-C22 Junta de Andalucía US-1263341 Junta de Andalucía P18-RT-2778
Databáze: OpenAIRE
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