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Multivariate time-series classification model based on enhanced multi-objective optimization algorithm
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DOI：10.1016/j.eswa.2025.130328.png)
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Abstract
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• A new NSGAII variant is proposed, which can efficiently solve a three-objective optimization problem by introducing the elite selection and water cycle mechanisms. • A multivariate time-series data classification framework is designed and considered as a multiobjective optimization problem that can jointly optimize early classification and channel selection. • Experiments are conducted on 25 benchmark functions and 12 multivariate time-series datasets from UEA and UCI. The results show that the proposed NSGAII variant, as well as the classification framework, outperform the existing mainstream methods in both optimization and time-series classification.
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