Genetic-algorithm-based Convolutional Neural Network for Robust Time Series Classification with Unreliable Data

Finding robust solutions to time series classification problems using deep neural networks has received wide attention. However, unreliable data makes classification very difficult. Traditional deep neural networks cannot effectively solve problems with strong noise. In this paper, we propose a hybr...

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Bibliographic Details
Published inSensors and materials Vol. 33; no. 4; p. 1149
Main Authors Wu, Jiang, Ji, Yanju, Li, Suyi
Format Journal Article
LanguageEnglish
Published Tokyo MYU Scientific Publishing Division 06.04.2021
Subjects
Artificial neural networks
Classification
Design optimization
Genetic algorithms
Neural networks
Robustness (mathematics)
Time series
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Summary:Finding robust solutions to time series classification problems using deep neural networks has received wide attention. However, unreliable data makes classification very difficult. Traditional deep neural networks cannot effectively solve problems with strong noise. In this paper, we propose a hybrid convolutional neural network (CNN) model combined with a genetic algorithm (GA) for time series classification (TSC) with unreliable data. To obtain a robust CNN structure, even though network structural optimization is an NP-hard problem, we design a GA for network structure optimization. Several benchmarks and actual datasets are adopted, and tests are carried out to prove the effectiveness of the proposed GA-based CNN. The numerical results show that our approach has better performance than other state-of-the-art deep neural networks.
ISSN:0914-4935
2435-0869
DOI:10.18494/SAM.2021.3002