## 2 Materials and Methods  
Based on the existing data rules, there is a limited impact on data classification [9]. Currently, several well-established methods incorporate random oversampling technology, which entails randomly duplicating data within data clusters containing limited amountsof data post-classification and integrating them into the database to balance the volume of data post-classification. Nonetheless, this approach tends towards rudimentary analysis and heavily emphasizes the entity problem, thereby inducing overfitting phenomena and affecting the data classification boundary [10]. Later researchers enhanced the oversampling technique through synthesis and introduced a synthetic minority oversampling technique. Its fundamental principle involves adding new data to the data cluster through linear distance interpolation. Although this data lacks substantial significance, the synthesized data are generally guaranteed to remain within the cluster boundary, and a safe zone can be ensured based on distance settings [11]. However, the synthetic oversampling technique is prone to being influenced by data noise. In scenarios with excessive data noise, it readily generates new noise points, which traditional data cleaning methods struggle to eliminate, ultimately impairing classification accuracy. Furthermore, this synthetic oversampling technique necessitates a relatively high level of expertise in parameter setting for synthetic data, encompassing not only algorithmic application experience but also industry data application experience. The setting of K value and c value in certain K-NN algorithms necessitates industry data analysis experience to ensure the precision of classification for synthetic data within the original clustering cluster, thereby preventing breaches of classification boundaries [12].  
Currently, this study of the noise impact of synthetic oversampling techniques remains a pivotal concern in the industry. B-SMOTE, for instance, employs a limited amount of boundary instance data as a foundation, refines sampling rules, and integrates data pertaining to a select few boundary data points. This approach exhibits improved resilience to noise, albeit with the presence of overlap. Subsequently, this concept has undergone continual evolution, resulting in adaptive synthetic sampling algorithms such as ADASYN, which leverage the k-value to ascertain the optimal allocation of weights. Nonetheless, all these algorithms hinge upon the utilization of the boundary to establish a so-called \"safe zone\" within which data synthesis and oversampling occur. However, the data synthesized in this manner inherently possesses a natural distance from the perimeter of the safe zone, thereby mitigating the influence of noise on the data. Nevertheless, this approach can expedite convergence during the classification process, potentially altering the centroid position of clusters, thereby affecting subsequent cluster analysis. Notably, this issue is not particularly apparent in traditional data samples; however, it becomes increasingly challenging in the processing of time-series data, logical data, and knowledge graph-based data, constituting an inherent limitation of the algorithm.