# Ad-RuLer: A Novel Rule-Driven Data Synthesis Technique for Imbalanced Classification
## 6 Conclusions
\begin{tabular}{c c c c} \hline
**Datasets** & **Approach** & **Resampling Parameter** & **Classifier Parameter** \\ \hline \multirow{6}{*}{Breast} & Ad-RuLer & d = 1, ratio = 1 & LR: penalty = 12', C = 1; \\  & SMOTE & k\_n = 3 & RF: n\_estimators = 500, max\_depth = 8, \\  & ADASYN & n\_n = 8 & min\_samples\_split = 5, min\_samples\_leaf = 3; \\  & Tome-links & None & XGB: max\_depth = 15, learning\_rate = 0.1, \\  & B-SMOTE & k\_n = 10, m\_n = 5 & n\_estimators = 500, reg\_lambda = 0.1; \\  & KMeansSMOTE & k\_n = 3, k\_e = 4 & n\_estimators = 500, reg\_lambda = 0.1; \\ \hline \multirow{6}{*}{Pima} & Ad-RuLer & d = 3, ratio = 0.1 & LR: penalty = 12', C = 1.5; \\  & SMOTE & k\_n = 5 & LR: penalty = 12', C = 1.5; \\  & ADASYN & n\_n = 10 & RF: n\_estimators = 600, max\_depth = 5, \\  & Tome-links & None & min\_samples\_split = 10, min\_samples\_leaf = 3; \\  & B-SMOTE & k\_n = 5, m\_n = 5 & XGB: max\_depth = 15, learning\_rate = 0.05, \\  & KMeansSMOTE & k\_n = 3, k\_e = 4 & n\_estimators = 500, reg\_lambda = 1; \\ \hline \multirow{6}{*}{Vehicle0} & Ad-RuLer & d = 12, ratio = 0.1 & \\  & SMOTE & k\_n = 5 & RF: n\_estimators = 500, max\_depth = 15, \\  & ADASYN & n\_n = 8 & min\_samples\_split = 15, min\_samples\_leaf = 3; \\  & Tome-links & None & XGB: max\_depth = 20, learning\_rate = 0.1 \\  & B-SMOTE & k\_n = 5, m\_n = 5 & n\_estimators = 600, reg\_lambda = 2; \\  & KMeansSMOTE & k\_n = 4, k\_e = 2 & n\_estimators = 600, max\_depth = 15, \\ \hline \multirow{6}{*}{Ecoli1} & Ad-RuLer & d = 4, ratio = 0.2 & \\  & SMOTE & k\_n = 8 & RF: n\_estimators = 600, max\_depth = 15, \\  & ADASYN & n\_n = 6 & min\_samples\_split = 20, min\_samples\_leaf = 10; \\  & Tome-links & None & XGB: max\_depth = 10, learning\_rate = 0.1, \\  & KMeansSMOTE & k\_n = 6, k\_e = 3 & n\_estimators = 300, reg\_lambda = 0.5; \\ \hline \multirow{6}{*}{Yeast3} & Ad-RuLer & d = 4, ratio = 0.1 & \\  & SMOTE & k\_n = 3 & RF: n\_estimators = 600, max\_depth = 20, \\  & ADASYN & n\_n = 6 & min\_samples\_split = 15, min\_samples\_leaf = 3; \\  & Tome-links & None & XGB: max\_depth = 20, learning\_rate = 0.1, \\  & B-SMOTE & k\_n = 6, k\_e = 2 & n\_estimators = 300, reg\_lambda = 0.1; \\ \hline \multirow{6}{*}{Yeast-0-2-5-7-9} & Ad-RuLer & d = 4, ratio = 0.2 & \\  & SMOTE & k\_n = 7 & RF: n\_estimators = 600, max\_depth = 25, \\  & ADASYN & n\_n = 10 & min\_samples\_split = 10, min\_samples\_leaf = 3; \\  & Tome-links & None & XGB: max\_depth = 10, learning\_rate = 0.1, \\  & KMeansSMOTE & k\_n = 10, k\_e = 3 & n\_estimators = 300, reg\_lambda = 2; \\ \hline \multirow{6}{*}{Abalone9} & Ad-RuLer & d = 6, ratio = 0.1 & \\  & SMOTE & k\_n = 5 & RF: n\_estimators = 500, max\_depth = 30, \\  & ADASYN & n\_n = 5 & min\_samples\_split = 5, min\_samples\_leaf = 3; \\  & B-SMOTE & k\_n = 3, m\_n = 10 & XGB: max\_depth = 20, learning\_rate = 0.1, \\  & KMeansSMOTE & k\_n = 10, k\_e = 4 & n\_estimators = 200, reg\_lambda = 0.5; \\ \hline \multirow{6}{*}{Car-good} & Ad-RuLer & d = 3, ratio = 0.3 & \\  & SMOTE & k\_n = 10 & RF: n\_estimators = 500, max\_depth = 40, \\  & ADASYN & n\_n = 5 & min\_samples\_split = 20, min\_samples\_leaf = 10; \\  & Tome-links & None & XGB: max\_depth = 5, learning\_rate = 0.01, \\  & B-SMOTE & k\_n = 8, m\_n = 3 & XGB: max\_depth = 5, learning\_rate = 0.01, \\  & KMeansSMOTE & k\_n = 12, k\_e = 4 & n\_estimators = 200, reg\_lambda = 3; \\ \hline \multirow{6}{*}{Winequality} & Ad-RuLer & d = 6, ratio = 0.1 & \\  & SMOTE & k\_n = 3 & RF: n\_estimators = 500, max\_depth = 20, \\  & ADASYN & n\_n = 5 & min\_samples\_split = 10, min\_samples\_leaf = 3; \\ \cline{1-1}  & Tome-links & None & XGB: max\_depth = 5, learning\_rate = 0.01, \\ \cline{1-1}  & B-SMOTE & k\_n = 8, k\_e = 3 & n\_estimators = 100, reg\_lambda = 0.5; \\ \hline \end{tabular}   
Abbreviations for datasets: Abalone9, Abalone9-18; Winequality, Winequality-red-4. Abbreviations for classifier: LR, Logistic Regression; RF, Random Forest; XGB, XGBoost. Abbreviations for parameter: k\_n, k\_neighbors; n\_n, n\_neighbors; m\_n, m\_n, m\_neighbors; k\_e, kmeans\_estimator.