Application of Gaussian distribution-based resampling technique on imbalanced food dataset
Tianle Li, Lei Sun, Enguang Zuo, Chen Chen · 2023
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.