# Effective Class-Imbalance Learning Based on SMOTE and Convolutional Neural Networks
## 4 Experimental Results
### Evaluation Metrics  
This section includes the elaboration of the metrics which is used to evaluate the performance of our proposed models. A fundamental classification metric tool is the Confusion Matrix. This tool is a way of demonstrating the number of correctly and incorrectly predicted samples by a classifier. It is usually a table that contains the actual and predicted state of samples compared to each other. Figure 11 depicts such a matrix for a binary  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Name of Parameter** & **Description** & **Choice** \\ \hline \multirow{3}{*}{Number of Jobs} & Corresponding to the number of & \\  & cores of Central Processing Unit & None \\  & (CPU) for use & \\ \hline \multirow{3}{*}{Number of features} & Corresponding to the number of & \multirow{3}{*}{Based on the number of features} \\  & input features & existent in each dataset \\ \hline \hline \end{tabular}
\end{table}
Table 4: _Cont._  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**No.** & **Name** & **Attribute** & **All Samples** & **Imbalanced Ratio** \\ \hline
1 & Wisconsin & 9 & 683 & 1.86 \\
2 & Pima & 8 & 768 & 1.87 \\
3 & iris0 & 4 & 150 & 2.00 \\
4 & glass0 & 9 & 214 & 2.06 \\
5 & glass1 & 9 & 214 & 1.82 \\
6 & glass6 & 9 & 214 & 6.38 \\
7 & yeast1 & 8 & 1484 & 2.46 \\
8 & Haberman & 3 & 306 & 2.78 \\
9 & vehicle1 & 18 & 846 & 2.90 \\
10 & vehicle2 & 18 & 846 & 2.88 \\
11 & vehicle3 & 18 & 846 & 2.99 \\
12 & ecoli1 & 7 & 336 & 3.36 \\
13 & ecoli2 & 7 & 336 & 5.46 \\
14 & ecoli3 & 7 & 336 & 8.6 \\
15 & new-thyroid1 & 5 & 215 & 5.14 \\
16 & new-thyroid2 & 5 & 215 & 5.14 \\
17 & segment0 & 19 & 2308 & 6.02 \\
18 & yeast3 & 8 & 1484 & 8.10 \\
19 & page-blocks0 & 10 & 5472 & 8.79 \\
20 & yeast-2\_vs\_4 & 8 & 514 & 9.08 \\
21 & penbased & 10 & 10,992 & 9.41 \\
22 & Nursery & 5 & 12,690 & 2.2 \\
23 & breast cancer & 117 & 102,294 & 163.2 \\
24 & Z-Alizadeh Sani & 55 & 303 & 2.48 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Datasets description in detail.  
classifier. This matrix includes four items, namely True Positive (TP), True Negative (TN), False Positive (FP), and False Negative (FN). The formal description of these four items is as follows:  
* True Positive (TP): The number of samples that belongs to the positive class and are correctly predicted as positive by the classifier
* True Negative (TN): The number of samples that belongs to the negative class and are correctly predicted as negative by the classifier
* False Positive (FP): Number of samples that belongs to the negative class, even though they are predicted as positive by the classifier
* False Negative (FN): Number of samples that belongs to the positive class, even though they are predicted as negative by the classifier  
Based on Figure 11, the classes of the minority and majority are remarked as positive and negative classes, respectively. Therefore, a confusion matrix is used to obtain performance metrics for the models on the imbalanced datasets. We utilized eight metrics, such as accuracy, precision, recall, F1-score, G-Mean, specificity, AUC-ROC, and Kappa, for evaluating the DNN and CNN models [29; 42; 67; 68; 69; 70; 71].