## 5 Experimental Results and Discussion
### Results and Discussion  
In order to compare the different feature selection methods with the method proposed in this paper, three indexes are used in this paper [53]. 1. Accuracy: Accuracy represents the ratio between the number of correct classification and the actual number, which is reflecting the accuracy of the classifier recognition results. It is an important index to evaluate algorithm performance;
2. The number of features: The number of features reflects ability of eliminate redundancy. It shows whether the method can find the optimal feature subset;
3. Fitness value: Fitness value can reflect the advantages and disadvantages of the solution selected by the classifier. The better fitness value can get the better the solution. Table 3 shows the mean and the standard deviation of the accuracy. Figure 3 presents the boxplot charts of the accuracy for eight used data sets. The boxplots are shown for the seventeen values of classification accuracy that are given by each algorithm at the end of the SVM training. The boxplots indicate the improved performance of EChOA for optimizing the SVM. The performance of EChOA is better than other optimization algorithms in terms of average accuracy. EChOA obtains the best performance on 13 data sets out of the 17 data sets, which accounts for 76.5% of the total data sets. For the other four data sets, EChOA also has strong competitiveness. Although EChOA do not achieve the highest accuracy in Coimbra, Dermatology and Glass, its classification accuracy ranked the third among the eight optimization algorithms, just next to GWO and WOA. It is noteworthy that the accuracy of EChOA is 10 percent higher than ChOA on Bupa data sets. It is 12 percent higher than GWO on the Knowledge data set and 18 percent higher than GWO on the Lymphography data set. At the same time, it can be seen from the box chart that each optimization algorithm generates ten accuracies in the process of running each data set ten times. From the perspective of their distribution, the accuracy of EChOA is very stable. The accuracy of the EChOA optimization algorithm hardly changes significantly in the 17 data sets. Especially in the Coimbra and Heart data sets, the EChOA optimization algorithm is still very stable in the case that the accuracy of almost all optimization algorithms varies significantly each time. All these show the superiority of the EChOA algorithm. Table 4 shows the mean and the standard deviation of the number of the features. Figure 4 presents the histogram of the number of features for all used data sets. We can intuitively see that the number of features required by EChOA is almost below the average of the number of features of all the algorithms. There are only two data sets do not get the best results (Bupa and Transfusion), which accounts for only 11.8 percent of the total. In Liver data set EChOA even requires only one feature. It shows that EChOA can use space more efficiently. In the analysis of classification accuracy, it has been found that EChOA's classification accuracy is lower than GWO and WOA in Coimbra, Dermatology and Glass. However, it is not difficult to find in Table 4 that the number of features required by EChOA in these three data sets is less than GWO and WOA. Especially in the Dermatology data set, EChOA required nearly seven fewer features than GWO and WOA. It can be seen in Figure 4, in Coimbra, Dermatology, Divorce and Liver data sets, there are two or three optimization algorithms with far higher features counts than the other optimization algorithms, but EChOA never had this situation. It should be noticed that the number of features required by CHOA is much higher than that of EChOA in Dermatology and Wine data sets, which also indicates the superiority of EChOA over CHOA and other algorithms in the quantitative aspect of features. Figure 3: Boxplot charts for the accuracy of EChOA and other algorithms based on 8 data sets. Table 5 shows the mean and the standard deviation of the fitness. Figure 5 presents the line chart of the fitness for six used data sets.