# Ad-RuLer: A Novel Rule-Driven Data Synthesis Technique for Imbalanced Classification
## 5 Discussion  
This study introduces a novel oversampling technique named Ad-RuLer. This method, grounded in the extraction of rules from datasets, aims to generate new data instances to enhance the classification performance of minority class instances in imbalanced datasets. Comprehensive experimental results reveal that, in comparison to prevailing oversampling algorithms, Ad-RuLer excels across various evaluation metrics, highlighting its potential application value and offering a novel approach to addressing data imbalance challenges.  
To delve deeper into Ad-RuLer's performance under varied dataset imbalances, datasets have been categorized based on their imbalance ratio (IR) into two groups: high imbalance (IR \(>\) 8) and low imbalance (IR \(\leq\) 8), with each group comprising five datasets. Figures 7 and 8 depict the average ranking results for these two groups, respectively. Evidently, Ad-RuLer leads in the AUC within the low imbalance group, secures the second place in both Precision and G-mean, and ranks third in F1 score. In the high imbalance group, Ad-RuLer dominates in the G-mean, AUC, and F1 score, and ranks second in Precision. Such findings underscore Ad-RuLer's robust performance, even in datasets with pronounced imbalances, without a deterioration in performance as the imbalance ratio increases, further accentuating its broad applicability.  
Figure 6: Critical differences between different resampling algorithms on all datasets using Nemenyi post hoc test. (**a**) Critical differences calculation for G-mean. (**b**) Critical differences calculation for F1 score. (**c**) Critical differences calculation for AUC. (**d**) Critical differences calculation for Precision.  
Figure 8: Average rankings of resampling methods in high IR dataset group.  
Figure 7: Average rankings of resampling methods in low IR dataset group.  
Additionally, when integrated with various classifiers, Ad-RuLer consistently demonstrates an exceptional predictive performance, emphasizing its reliability and adaptability. Based on the results of the Wilcoxon Signed-rank test, we observed that under the logistic regression classifier, Ad-RuLer exhibits a more significant difference compared to other algorithms, particularly in the AUC, where Ad-RuLer significantly outperforms all other methods.  
Nevertheless, this research has its limitations. The impact of Ad-RuLer's performance under some specific scenarios, such as in high-dimensional datasets or where class overlaps are prominent, has not been thoroughly explored. Future research will delve into the performance of Ad-RuLer in such scenarios. Moreover, this study evaluated Ad-RuLer's performance using ten datasets. To provide a more holistic assessment of Ad-RuLer's capabilities, subsequent research aims to test across a wider array of datasets. Furthermore, there is an intent to deploy Ad-RuLer in specialized application domains, such as addressing the severe data imbalance problem in the medical domain and considering to integrate it with other techniques, such as cost-sensitive learning, to further enhance its efficacy.