# Ad-RuLer: A Novel Rule-Driven Data Synthesis Technique for Imbalanced Classification
## 6 Conclusions  
In this study, it has been demonstrated that Ad-RuLer, an approach based on inductive rule learning, can be used as an alternative option to conventional methodologies for classifying imbalanced data. As an efficient oversampling method, Ad-RuLer substantially enhances the predictive performance of the classifier when confronted with imbalanced datasets. Comparative experiments with other data resampling methods on ten real-world datasets with different imbalance ratios demonstrate the effectiveness and potential of our approach. Future research will focus on assessing the efficacy of Ad-RuLer in high-dimensional datasets and scenarios with significant class overlap, expanding the scope to address practical problems in specialized domains, such as healthcare and financial fraud, where data imbalance issues are more pronounced. Furthermore, the potential enhancement of Ad-RuLer in complex data settings will be investigated via its integration with cost-sensitive learning techniques and dimensionality reduction techniques, such as PCA and t-SNE. Conceptualization, X.Z., A.N., I.P., F.M. and E.R. ; methodology, X.Z. and A.N. ; software, X.Z. and I.P. ; validation, X.Z. and A.N. ; formal analysis, X.Z. and A.N. ; investigation, X.Z., I.P. and A.N. ; resources, A.N., F.M. and E.R. ; data curation, X.Z. ; writing--original draft preparation, X.Z. ; writing--review and editing, X.Z. and A.N. ; supervision, A.N., F.M. and E.R. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. These links for datasets: [[https://sci2s.ugr.es/keel/imbalanced.php](https://sci2s.ugr.es/keel/imbalanced.php)]([https://sci2s.ugr.es/keel/imbalanced.php](https://sci2s.ugr.es/keel/imbalanced.php)) (accessed on 18 September 2023), [[https://www.kaggle.com/datasets](https://www.kaggle.com/datasets)]([https://www.kaggle.com/datasets](https://www.kaggle.com/datasets)) (accessed on 18 September 2023). The authors declare no conflict of interest. The following abbreviations are used in this manuscript: \begin{tabular}{l l} SMOTE & Synthetic Minority Oversampling Technique \\ ADASYN & Adaptive Synthetic Oversampling \\ RUS & Random Undersampling \\ ROS & Random Oversampling \\ TPR & True Positive Rate \\ FPR & False Positive Rate \\ \end{tabular} \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Datasets** & **Algorithms** & **G-Mean** & **F1 Score** & **AUC** & **Precision** \\ \hline Heart Disease & Ad-RuLer & **0.743** & **0.690** & **0.823** & **0.691** \\  & SMOTE & 0.729 & 0.672 & 0.810 & 0.673 \\  & ADASYN & 0.731 & 0.674 & 0.812 & 0.680 \\  & Tomek-links & 0.734 & 0.677 & 0.815 & 0.689 \\  & Borderline-SMOTE & 0.734 & 0.677 & 0.810 & 0.681 \\  & KmeansSMOTE & 0.735 & 0.678 & 0.814 & 0.687 \\ \hline Breast Cancer & Ad-RuLer & 0.947 & 0.936 & 0.988 & 0.946 \\  & SMOTE & **0.959** & **0.950** & **0.992** & 0.954 \\  & ADASYN & **0.959** & 0.946 & **0.992** & 0.935 \\  & Tomek-links & 0.954 & 0.943 & 0.991 & 0.944 \\  & Borderline-SMOTE & 0.952 & 0.940 & 0.991 & 0.942 \\  & KMeansSMOTE & 0.957 & 0.948 & 0.991 & **0.957** \\ \hline Pima & Ad-RuLer & **0.726** & **0.649** & **0.809** & 0.626 \\  & SMOTE & 0.720 & 0.641 & 0.804 & 0.623 \\  & ADASYN & 0.717 & 0.637 & 0.800 & 0.611 \\  & Tomek-links & 0.722 & 0.643 & 0.808 & 0.630 \\  & Borderline-SMOTE & 0.706 & 0.624 & 0.796 & 0.602 \\  & KmeansSMOTE & 0.715 & 0.636 & 0.808 & **0.647** \\ \hline Vehicle0 & Ad-RuLer & 0.955 & 0.933 & **0.993** & 0.909 \\  & SMOTE & 0.965 & **0.935** & 0.992 & 0.913 \\  & ADASYN & **0.968** & **0.935** & 0.992 & 0.904 \\  & Tomek-links & 0.964 & 0.934 & 0.991 & 0.913 \\  & Borderline-SMOTE & 0.962 & 0.933 & **0.993** & **0.914** \\  & KMeansSMOTE & 0.961 & 0.930 & 0.992 & 0.911 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance comparison between different data resampling approaches using XGBoost.