Application of Gaussian distribution-based resampling technique on imbalanced food dataset

Tianle Li; Lei Sun; Enguang Zuo; Chen Chen

doi:10.1117/12.3011805

7 December 2023 Application of Gaussian distribution-based resampling technique on imbalanced food dataset

Tianle Li, Lei Sun, Enguang Zuo, Chen Chen

Proceedings Volume 12941, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2023); 129411U (2023) https://doi.org/10.1117/12.3011805
Event: Third International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 203), 2023, Yinchuan, China

Abstract

The imbalanced food dataset is one of the various imbalanced datasets that are commonly found in the real world. For imbalanced datasets, we usually want algorithms to identify minority class samples in the imbalanced datasets, but traditional classification algorithms often suffer from performance degradation, overfitting and other problems. To address these problems, we propose a method based on Gaussian distribution resampling combined with a machine learning classifier. The method generates new samples in the form of Gaussian distribution by metering minority class samples in the unbalanced dataset, and after selecting the appropriate anchor samples, a balanced dataset is obtained. Finally, the balanced dataset is classified by a classifier. By conducting experiments on two real imbalanced food datasets, our method outperformed the classification performance of mainstream resampling methods on both datasets, achieving the best AUC and G-mean values. The experimental results demonstrate that our method can effectively identify minority class samples in the imbalanced food dataset and has some application value in the food field.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Tianle Li, Lei Sun, Enguang Zuo, and Chen Chen "Application of Gaussian distribution-based resampling technique on imbalanced food dataset", Proc. SPIE 12941, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2023), 129411U (7 December 2023); https://doi.org/10.1117/12.3011805

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KEYWORDS

Machine learning

Detection and tracking algorithms

Visualization

Matrices

Overfitting

Distance measurement

Inspection

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