## 3 Results
### Comparison of the Performance of Classifiers Used in 500 Iterations  
After evaluating the performance of the various classifiers in the best state of training, the validity of the classifiers should now be evaluated, especially the majority voting method. The validity of the classifiers is determined by the results of them in different iterations, thus, if the classifier results are close together in different iterations, then it can be claimed that the classifier is valid. Table 12 shows the confusion matrix, the classification error per class, and the correct classification rate of the different classifiers at 500 iterations. The highest rate of correct classification was related to the majority voting method, which was 97.20% (Table 13). Figure 2 illustrates a box diagram of area under the ROC curve obtained by different classifiers at 500 iterations. As can be seen, with the exception of the hybrid ANN-FA classifier, the other classifiers have fully compressed box diagrams, indicating close proximity to the results in different iterations. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Classifier** & **Class** & **Recall (\%)** & **Specificity (\%)** & **Precision (\%)** & **F\_Measure (\%)** & **AUC (Mean \(\pm\) Std. dev.)** & **Accuracy (Mean \% \(\pm\) Std. dev.)** \\ \hline \multirow{2}{*}{ANN-BA} & Fruit & 96.18 & 96.76 & 96.78 & 96.48 & 0.9956 \(\pm\) \\  & Backgr. & 96.76 & 96.18 & 96.16 & 96.46 & 0.0007 & 96.47 \(\pm\) 0.5657 \\ \hline \multirow{2}{*}{ANN-BBO} & Fruit & 96.19 & 96.73 & 96.74 & 96.47 & 0.9956 \(\pm\) \\  & Backgr. & 96.73 & 96.19 & 96.17 & 96.45 & 0.0007 & 96.46 \(\pm\) 0.5167 \\ \hline \multirow{2}{*}{ANN-FA} & Fruit & 96.79 & 97.02 & 97.02 & 96.91 & 0.9691 \(\pm\) \\  & Backgr. & 97.02 & 96.79 & 96.78 & 96.90 & 0.0046 & 96.91 \(\pm\) 0.4572 \\ \hline \multirow{2}{*}{Voting} & Fruit & 97.61 & 96.80 & 96.77 & 97.19 & 0.9958 \(\pm\) \\  & Backgr. & 96.80 & 97.61 & 97.63 & 97.21 & 0.0008 & 97.20 \(\pm\) 0.4917 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: The performance of different classifiers based on different criteria for 500 iterations. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Class** & **Recall (\%)** & **Specificity (\%)** & **Precision (\%)** & **F\_Measure (\%)** & **AUC** & **Accuracy** \\ \hline \multirow{2}{*}{Fruit Background} & Fruit & 99.17 & 96.91 & 96.83 & 97.99 & \multirow{2}{*}{0.9970} & \multirow{2}{*}{98.01} \\  & Backgr. & 96.91 & 99.17 & 99.19 & 98.04 & & \\ \hline \end{tabular}
\end{table} 
Table 11: The performance of the MV method based on different criteria in the best state of training for the testing data. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Classification** & **Real/** & \multirow{2}{*}{**Fruit**} & \multirow{2}{*}{**Background**} & \multirow{2}{*}{**Total Data**} & **Classification Error** & **Correct Classification** \\
**Method** & **Obtained Class** & & & & **per Class (\%)** & **Rate (\%)** \\ \hline \multirow{2}{*}{ANN-BA} & Fruit & 1,742,085 & 57,915 & 1,800,000 & 3.22 & \multirow{2}{*}{96.47} \\  & Backgr. & 69,102 & 1,730,898 & 1,800,000 & 3.84 & \multirow{2}{*}{96.46} \\ \hline \multirow{2}{*}{ANN-BBO} & Fruit & 1,741,427 & 58,573 & 1,800,000 & 3.25 & \multirow{2}{*}{96.46} \\  & Backgr. & 68,847 & 1,731,153 & 1,800,000 & 3.82 & \\ \hline \multirow{2}{*}{ANN-FA} & Fruit & 1,746,422 & 53,578 & 1,800,000 & 2.98 & \multirow{2}{*}{96.91} \\  & Backgr. & 57,786 & 1,742,214 & 1,800,000 & 3.21 & \\ \hline \multirow{2}{*}{Voting} & Fruit & 1,741,920 & 58,080 & 1,800,000 & 3.23 & \multirow{2}{*}{97.20} \\  & Backgr. & 42,643 & 1,757,357 & 1,800,000 & 2.37 & \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Confusion matrices, classification error per class, and correct classification rate (CCR) of different classifiers for 500 iterations. Figure 3 shows a box diagram of the correct classification rate obtained by different classifiers in 500 iterations. The graph shows that the majority voting method, with the exception of a few repeats, was above 97%.