⚡ electrical engineering
A Posterior Probability-Guided Kernel-Space Oversampling Machine Learning Method for Imbalanced Rolling Bearing Health Monitoring
This paper proposes Kernel-PAO, a novel machine learning method that shifts oversampling to kernel space using posterior probability-guided interpolation to effectively address severe class imbalance in rolling bearing health monitoring, significantly outperforming traditional techniques like SMOTE in accuracy and robustness.
Original paper licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer
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