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Automated machine learning approach for time series classification pipelines using evolutionary optimization
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AI
DOI：10.1016/j.knosys.2023.110483.png)
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Abstract
En 中文
Automated machine learning has the ability to improve the efficiency of time series classification due to the ability to combine multiple feature extraction methods and classification models. In the paper, we propose a flexible AutoML approach that combines multiple feature generation strategies (spectral, wavelet, topological, quantile) and classifiers as parts of the modeling pipeline. It allows obtaining a more robust and lightweight solution for the time series classification problem. Generation of the pipeline is based on an evolutionary algorithm.Comparison with approaches with the highest results was conducted on the UEA/UCR archive for modeling quality analysis. The proposed approach allows, on the one hand, to solve the task of time series classification automatically and could be used as part of an industrial data processing pipeline. On the other hand, it could be used as a tool for modeling and studying natural process properties described as time series.(c) 2023 Elsevier B.V. All rights reserved.
Keywords:
Time series classification
AutoML
Machine learning
Composite AI
Journal
K
IF:
7.6
Papers:
1.3W
Citations:
4.5W
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