Abstract
Prediction strategies based on transfer learning have been proved to be effective in solving Dynamic Multi-Objective Optimization Problems (DMOPs). However, slow running speed impedes the development of this method. To address this issue, this paper proposes a transfer learning method based on imbalanced data classification and combines it with a decomposition-based multi-objective optimization algorithm, referred to as ICTr-MOEA/D. This method combines the prediction strategies based on transfer learning with knee points to save computational resources. In order to prevent the prediction accuracy from being affected by the insufficient of knee points, ELM classifier selects more high-quality points from a large number of random solutions. Moreover, SMOTE resolves the class imbalance problem during the ELM training process. The simulation results demonstrate that ICTr-DMOEA shows good competitiveness.
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Acknowledgement
This work was supported by the National Key Research and Development Project under Grant 2021ZD0112002, the National Natural Science Foundation of China under Grants 61973010, 62021003 and 61890930-5.
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Wang, D., Yang, C. (2023). Dynamic Multi-objective Prediction Strategy for Transfer Learning Based on Imbalanced Data Classification. In: Zhang, H., et al. International Conference on Neural Computing for Advanced Applications. NCAA 2023. Communications in Computer and Information Science, vol 1869. Springer, Singapore. https://doi.org/10.1007/978-981-99-5844-3_31
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DOI: https://doi.org/10.1007/978-981-99-5844-3_31
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