## 6 Conclusions and Future Works  
This study introduces a novel methodology that combines contrastive learning with the SMOTE-RSB oversampling technique to tackle the issue of imbalanced image classification. Contrastive learning enables the generation of robust feature representations without the need for explicit labels. This results in the production of features capable of adapting effectively to variations in input. The pre-trained encoder from the MoCo framework is employed to extract features that effectively manage class imbalances in image datasets. Furthermore, we utilise SMOTE-RSB oversampling within the feature space to reinforce under-represented classes and eliminate irrelevant features, thereby enhancing the overall utility of the feature set. With the balanced dataset obtained through the feature extraction and oversampling phases, the final step involves classification using an MLP. The efficacy of our method is subjected to rigorous testing on artificially imbalanced datasets such as CIFAR-10, SVHN, and ImageNet-LT. The proposed method yielded an improvement of 10% in the F1 and Gmean score in CIFAR-10-ST, 20% in CIFAR-10-LT, and a 10% increase in Top-1 accuracy on ImageNet-LT compared to the current state-of-the-art methods.The  
Figure 11: Different oversampling methods with IR = 50 on the CIFAR-10 dataset. (**a**) F1 score. (**b**) G-mean score. results of our qualitative and quantitative assessments indicate the method's effectiveness in addressing imbalanced image datasets. Notably, the results obtained from the ImageNet-LT dataset confirm the method's applicability to large-scale real-world scenarios. Further insights were gained through an ablation study that tested various oversampling techniques and contrastive learning frameworks, confirming the model's versatility and adaptability. Although it is distinctive in that it generates data in representation spaces, the proposed method is a pre-processing method. It is worthy of note that the computational expense involved in processing extensive datasets is considerable. Moreover, the efficacy of this approach is contingent upon the quality of the features extracted by the MoCo model. In order to overcome these limitations, we will focus on optimizing the encoder and explore the utilization of prototype-based contrastive learning, which employs learned prototypes to encapsulate complete categories, thereby emphasizing compactness within categories and differentiation between them. The findings of our research will inform the development of robust methodologies for the analysis of industrial processes characterized by the presence of outliers. The method helps workers to more accurately identify atypical types. Forthcoming enhancements will offer effective technical assistance for addressing a broader spectrum of class imbalance issues. Conceptualization, N.J. and M.I.R. ; methodology, X.G. ; software, X.G. ; validation, N.J. and M.I.R. ; formal analysis, X.G. ; resources, M.I.R. ; writing--original draft preparation, X.G. ; writing--review and editing, N.J. All authors have read and agreed to the published version of the manuscript. This work is funded by scientific research project of Xinhua College of Ningxia University (grant number 24 XHKY02). Not applicable. The data that support the findings of this study are openly available in CIFAR-10 at [[http://www.cs.toronto.edu/~kriz/cifar.html](http://www.cs.toronto.edu/~kriz/cifar.html)]([http://www.cs.toronto.edu/~kriz/cifar.html](http://www.cs.toronto.edu/~kriz/cifar.html)), accessed on 25 November 2024; The data that support the findings of this study are available in SVHN at [[http://ufldl.stanford.edu/housenumbers](http://ufldl.stanford.edu/housenumbers)]([http://ufldl.stanford.edu/housenumbers](http://ufldl.stanford.edu/housenumbers)), accessed on 25 November 2024; The data that support the findings of this study are available in ImageNet at [[https://www.image-net.org/](https://www.image-net.org/)]([https://www.image-net.org/](https://www.image-net.org/)), accessed on 25 November 2024.