## 3 The Proposed Algorithm
The solution is represented as seven features followed with two variables \(M\), and \(CF\) as shown in Table 6. An initial solution \(\alpha\) is randomly generated according to the representation of solution in Table 6. For each generation, the next solution \(\beta\) is generated from \(\alpha\) by randomly swapping these seven features and randomly generating these values of four variables in the current solution. \(T\) is decreased after running \(I_{gen}\) generations, according to a formula \(T\leftarrow\lambda T\), where \(0<\lambda<1\). Let \(obj(\alpha)\) denotes the testing accuracy of \(\alpha\), and \(\Delta\) denote the difference between \(obj(\alpha)\) and \(obj(\beta)\); that is \(\Delta=obj(\alpha)-obj(\beta)\). The probability of replacing \(\alpha\) with \(\beta\), where \(\alpha\) is the current solution and \(\beta\) is the next solution, given that \(\Delta>0\), is \(e^{-\Delta/T}\). This is accomplished by generating a random number \(r\in\) [0,1] and replacing the solution with \(\beta\) if \(<\!\!e^{-\Delta/T}\). Meanwhile, if \(\Delta\leq 0\), the probability of replacing \(\alpha\) with \(\beta\) is one. In the proposed algorithm, SA and DT are performed to optimize parameters (\(M\) and \(CF\)) to increase the testing accuracy for selected features and build the decision rules. The proposed algorithm is repeated until \(T\) is lower than \(T_{f}\). Thereafter, the best testing accuracy, and decision rules are reported. Figure 1: A flow chart of the proposed algorithm. The proposed approach uses the accuracy based on the confusion matrix, which can test the performance of the classification method. The confusion matrix is shown as Table 7.  
TP, FP, FN, and TN represent true positive class, false positive class, false negative class, and true negative class, respectively. The predicted value is a positive example, which is recorded as P (positive). The predicted value is a negative example, which is recorded as N (negative). When the predicted value is the same as or opposite to the actual value, they are recorded as T (true) or F (false), respectively. Four results of defining of examples in the dataset after model classification are: TP: predicted positive class or actually positive class; FP: predicted positive class or actually negative class; TN: predicted negative class or actually negative class; FN: predicted negative class or actually positive class. The classification accuracy calculation formula is as follows:  
\[\text{Classification accuracy}=(\text{TP}+\text{TN})/(\text{TP}+\text{ FN}+\text{FP}+\text{TN})\times 100\% \tag{5}\]  
The receiver operating characteristic curve (ROC curve) and area under the curve (AUC) can test the performance of classification results. Because ROC curve has a good characteristic, when the distribution of positive and negative samples in the test set is changed, ROC curve can be still unchanged. Class imbalance often occurs in the actual data set, that is, there are many more negative samples than positive samples (or vice versa), and the distribution of positive and negative samples in the test data may change with time. The area under ROC curve is calculated as the evaluation method of imbalanced data. It can comprehensively describe the performance of classifier under different decision thresholds. AUC calculation formula is as follows:  
\[\text{AUC}=\frac{1+\left(\frac{\text{TP}}{\text{FP}+\text{FN}}\right)-\left( \frac{\text{FP}}{\text{TN}+\text{FP}}\right)}{2} \tag{6}\]