# Effective Class-Imbalance Learning Based on SMOTE and Convolutional Neural Networks
## 5 Discussion
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Study** & **Method** & **Acc (\%)** & **Rec (\%)** & **F1 (\%)** & **Pre (\%)** & **Spe (\%)** & **AUC (\%)** \\ \hline [PERSON] & \multirow{2}{*}{Sequential Minimal Optimization} & \multirow{2}{*}{92.09} & \multirow{2}{*}{97.22} & \multirow{2}{*}{NC} & \multirow{2}{*}{NC} & \multirow{2}{*}{79.31} & \multirow{2}{*}{NC} \\ et al. 2012 [72] & & & & & & \\ [PERSON] & \multirow{2}{*}{Ensemble of Naive Bayes and} & \multirow{2}{*}{88.52} & \multirow{2}{*}{91.12} & \multirow{2}{*}{NC} & \multirow{2}{*}{NC} & \multirow{2}{*}{82.05} & \multirow{2}{*}{NC} \\ et al. 2012 [73] & & & & & & \\ [PERSON] & \multirow{2}{*}{Information gain + Sequential} & \multirow{2}{*}{94.08} & \multirow{2}{*}{96.30} & \multirow{2}{*}{NC} & \multirow{2}{*}{NC} & \multirow{2}{*}{88.51} & \multirow{2}{*}{NC} \\ et al. 2013 [74] & & & & & & \\ [PERSON] et al., & \multirow{2}{*}{Support vector machine} & \multirow{2}{*}{86.67} & \multirow{2}{*}{NC} & \multirow{2}{*}{NC} & \multirow{2}{*}{NC} & \multirow{2}{*}{NC} & \multirow{2}{*}{NC} \\ et al. 2017 [75] & & & & & & \\ [PERSON] et al., & \multirow{2}{*}{Neural network + Genetic algorithm} & \multirow{2}{*}{93.85} & \multirow{2}{*}{97.00} & \multirow{2}{*}{NC} & \multirow{2}{*}{NC} & \multirow{2}{*}{92.00} & \multirow{2}{*}{NC} \\ et al. 2017 [76] & & & & & & \\ [PERSON] et al., 2018 [77] & & & & & & & \\ [PERSON] et al., & \multirow{2}{*}{Naive Bayes + Genetic algorithm} & \multirow{2}{*}{88.16} & \multirow{2}{*}{88.00} & \multirow{2}{*}{NC} & \multirow{2}{*}{NC} & \multirow{2}{*}{87.78} & \multirow{2}{*}{NC} \\ et al. 2019 [78] & & & & & & & \\ [PERSON] et al., & \multirow{2}{*}{Classification + genetic search} & \multirow{2}{*}{94.66} & \multirow{2}{*}{94.70} & \multirow{2}{*}{94.70} & \multirow{2}{*}{NC} & \multirow{2}{*}{96.60} \\ et al. 2019 [79] & & & & & & & \\ [PERSON] et al., & \multirow{2}{*}{N2 Genetic optimizer-nuSupport Vector Machine} & \multirow{2}{*}{93.08} & \multirow{2}{*}{NC} & \multirow{2}{*}{91.51} & \multirow{2}{*}{NC} & \multirow{2}{*}{NC} & \multirow{2}{*}{NC} \\ et al. 2019 [80] & & & & & & & \\ [PERSON] et al., & \multirow{2}{*}{Neural network + Gini Index for} & \multirow{2}{*}{88.49} & \multirow{2}{*}{NC} & \multirow{2}{*}{NC} & \multirow{2}{*}{NC} & \multirow{2}{*}{NC} & \multirow{2}{*}{NC} \\ et al. 2019 [81] & & & & & & & \\ [PERSON] et al., & \multirow{2}{*}{Ensemble Classifier with [PERSON]} & \multirow{2}{*}{92.07} & \multirow{2}{*}{94.00} & \multirow{2}{*}{NC} & \multirow{2}{*}{87.40} & \multirow{2}{*}{95.30} \\ et al. 2019 [81] & & & & & & & \\ [PERSON] et al., & \multirow{2}{*}{detection algorithm + SMOTE +} & \multirow{2}{*}{92.58} & \multirow{2}{*}{92.99} & \multirow{2}{*}{90.62} & \multirow{2}{*}{92.59} & \multirow{2}{*}{NC} & \multirow{2}{*}{NC} \\ et al. 2020 [82] & & & & & & & \\ [PERSON] and & \multirow{2}{*}{Optimization-emotional neural networks coupled with feature selection} & \multirow{2}{*}{88.34} & \multirow{2}{*}{91.85} & \multirow{2}{*}{92.12} & \multirow{2}{*}{92.37} & \multirow{2}{*}{78.98} & \multirow{2}{*}{NC} \\ et al. 2020 [83] & & & & & & & \\ [PERSON] et al., & \multirow{2}{*}{Classification and Regression tree} & \multirow{2}{*}{92.41} & \multirow{2}{*}{98.61} & \multirow{2}{*}{NC} & \multirow{2}{*}{NC} & \multirow{2}{*}{77.01} & \multirow{2}{*}{NC} \\ et al. 2020 [84] & & & & & & & \\ [PERSON] et al., & \multirow{2}{*}{Random trees} & \multirow{2}{*}{91.47} & \multirow{2}{*}{NC} & \multirow{2}{*}{NC} & \multirow{2}{*}{NC} & \multirow{2}{*}{NC} & \multirow{2}{*}{96.70} \\ et al. 2020 [85] & & & & & & & \\ [PERSON]- & \multirow{2}{*}{Hybrid Particle Swarm Optimization} & \multirow{2}{*}{84.25} & \multirow{2}{*}{NC} & \multirow{2}{*}{NC} & \multirow{2}{*}{NC} & \multirow{2}{*}{NC} & \multirow{2}{*}{NC} \\ et al. 2021 [86] & & & & & & & \\ [PERSON] et al., & \multirow{2}{*}{Support Vector Machine -Extreme} & \multirow{2}{*}{93.86} & \multirow{2}{*}{NC} & \multirow{2}{*}{91.86} & \multirow{2}{*}{93.86} & \multirow{2}{*}{NC} & \multirow{2}{*}{NC} \\ et al. 2021 [87] & & & & & & & \\ [PERSON] et al., \multirow{2}{*}{Extreme gradient boosting + Feature} & \multirow{2}{*}{94.70} & \multirow{2}{*}{96.10} & \multirow{2}{*}{94.60} & \multirow{2}{*}{93.40} & \multirow{2}{*}{93.20} & \multirow{2}{*}{98.00} \\ et al. 2022 [71] & & & & & & & \\ [PERSON] et al., \multirow{2}{*}{Bit Algorithm + Ensemble of} & \multirow{2}{*}{97.37} & \multirow{2}{*}{98.15} & \multirow{2}{*}{98.15} & \multirow{2}{*}{NC} & \multirow{2}{*}{95.45} & \multirow{2}{*}{96.80} \\ et. 2022 [88] & & & & & & & \\ \hline The proposed & \multirow{2}{*}{Mixed SMOTE-NORM.-CNN} & \multirow{2}{*}{98.57} & \multirow{2}{*}{98.58} & \multirow{2}{*}{98.57} & \multirow{2}{*}{98.58} & \multirow{2}{*}{98.42} & \multirow{2}{*}{99.14} \\ study & & & & & & & & \\ \hline \hline \end{tabular}
\end{table}