## 4 Experiment
### ISmote Algorithm Performance Test  
In order to verify the efficacy of the ISmote algorithm in practical applications, comparative experiments were conducted on unbalanced data sets with the Smote algorithm, Borderline-Smote [28] algorithm, SVMSmote [29] algorithm, ADASYN [30] algorithm, RSmote algorithm, and Smote-IFF algorithm. The experimental results are shown in Figure 11. While the Smote algorithm alleviates the issue of data imbalance to a certain extent, the newly synthesised samples that invade the majority class area result in the production of noise samples and the overlapping of these with the original minority class samples. Three algorithms, Borderline-Smote, SVMSmote, and ADASYN, all concentrate on the boundary region for sample generation. Nevertheless, the newly synthesised samples still exhibit some degree of overlap with the majority of classes, particularly in the boundary region, where the issue of noisy samples is particularly pronounced. Furthermore, although the RSmote algorithm and the Smote-IFF algorithm are capable of avoiding the generation of noise to a certain extent, the newly synthesised samples lack sufficient diversity and are unable to fully expand the coverage of the minority class in space. In contrast, the ISmote algorithm not only alleviates the imbalance problem but also effectively prevents the newly synthesised samples from invading the majority class area, significantly reducing the impact of noise. Moreover, the newly synthesised samples exhibit a high degree of diversity, thereby providing the classifier with a greater quantity of classification information. In order to conduct a more objective evaluation, a series of comparative experiments were conducted on five imbalanced datasets sourced from the public KEEL database. The detailed information is presented in Table 2. In the experimental session, the Random Forest (RF) [31] algorithm and the K-Nearest Neighbor (KNN) [32] algorithm were employed to train data processed by various sampling methods. This study employs key performance metrics, including the F1 score, G-mean, and AUC to assess the performance variations of different sampling algorithms when applied to the same classifier. As demonstrated in Tables 3 and 4, an analysis of the results reveals that the ISmote algorithm outperforms other sampling methods in terms of performance. In particular, the ISmote algorithm achieved the highest F1 score, G-mean, and AUC across multiple datasets for both KNN and RF classifiers. This indicates that the ISmote algorithm significantly enhances the generalisation ability of classifiers by increasing the diversity of minority class samples, which effectively enhances the overall classification performance of the model, particularly when dealing with highly imbalanced datasets. Nevertheless, it should be noted that the ISmote algorithm does not consistently outperform other sampling methods across all datasets. The distinctive characteristics of each dataset, including its features and feature spaces, can influence the effectiveness of any sampling method. Figure 11: Effect of synthesising samples. (**a**) Original data; (**b**) Smote algorithm; (**c**) Borderline-Smote algorithm; (**d**) SVMSmote algorithm; (**e**) ADASYN algorithm; (**f**) Smote-IPF algorithm; (**g**) RSmote algorithm; and (**h**) ISmote algorithm. \begin{table}
\begin{tabular}{c c c c} \hline
**Dataset** & **Sample Number** & **Feature Dimension** & **Imbalance Rate/\%** \\ \hline WIS & 683 & 9 & 1.86 \\ LED & 443 & 7 & 10.97 \\ ECO & 336 & 7 & 15.80 \\ YEA5 & 1484 & 8 & 32.73 \\ YEA6 & 1484 & 8 & 41.40 \\ \hline \end{tabular}
\end{table} 
Table 2: Information on public datasets.