## 5 Results and Discussion  
### Results of Approach 1  
Tables 3-8 show the approaches to the results of approach 1. The top 10 CIs and their averaged accuracies for each data set are shown in Tables 3, 5 and 7. Tables 4, 6 and 8 show the accuracies per class for CI_TL, CI_FL and CI_TFL, respectively. In Table 3, the best accuracy of 92.89%, was obtained by using CI_TL with 7 CIs. Otherwise, to CI_TR a maximum accuracy of 95.17% was obtained with 8 CIs. Per-class accuracy values for CI_TL are presented in Table 4. The best performance with 10 attributes shows that class 1 has the highest classification accuracy over 98% followed by class 2 with accuracy over 93%. Classes 3 and 4 have similar classification accuracy over 86%. Class 3 does not reach accuracy over 89% in any case. Class 4 can reach accuracy over 92% opposite to the accuracy of classes 1 and 2. Regarding the top 10 of time domain CIs presented in the Table 3, nine of the 10 are common: zero crossing, wave length, SSC, WAMP, kurtosis, energy operator, CPT3, shape factor, and skewness. In Table 5, the best accuracy of 91.29% was obtained using CI_FL with 7 CIs. By contrast, to CI_FR a maximum accuracy of 94.36% was obtained with 9 CIs. Per-class accuracy values for CI_FL are presented in Table 6. The best performance with 10 attributes shows that class 4 has the highest classification accuracy over 98% followed by class 2 with accuracy over 89%. Classes 1 and 3 have similar classification accuracy over 85%. Classes 1, 2 and 3 do not reach accuracy over 89% in any case. Regarding the top 10 frequency domain CIs presented in the Table 5, seven of the 10 are common PKF, CP2, FR, skewnessf, centroid of spectrum, VCF, and spectrum spread. In Table 7, the best accuracy of 96.62% due to its low std 0.59, was obtained using CI_TFL with 10 CIs. By contrast, to CI_TFR a maximum accuracy of 97.14% was obtained with also 10 CIs. Per-class values forCI_TFL are presented in Table 8. The best performance with 10 attributes shows that classes 1 and 4 have similar high classification accuracy over 97%. Classes 2 and 3 have also similar classification accuracy over 95%. From the use of seven attributes, all the classes reach classification accuracy over 90%. Regarding the top 10 time + frequency domain CIs presented in the Table 7, six of the 10 are common; four belong to the frequency domain: PKF, CP2, FR, SM4; and two to the time domain: zero crossing and SCC. This result shows that the use of combined features from time and frequency domains can improve the classification accuracy for each class. To evaluate the CIs in time, frequency and time + frequency domain, we compare the accuracy of each ranked feature set. Figure 5 shows the accuracy results for each data set. Here, the X-axis represents the number of \(k\) first CIs most important selected by RF. The Y-axis represents the average accuracy of the \(k\) first CIs. \begin{table}
\begin{tabular}{l l c c c c} \hline \hline \multirow{2}{*}{\#} & \multirow{2}{*}{**CIs**} & \multicolumn{5}{c}{**Classes (Accuracy\%)**} \\ \cline{3-6}  & & **1** & **2** & **3** & **4** \\ \hline
1 & Zero crossing & 100 & 0 & 0 & 0 \\ \hline
2 & Wave length & 65.95 & 55.44 & 48.58 & 71.92 \\ \hline
3 & Kurtosis & 86.47 & 74.04 & 71.58 & 74.06 \\ \hline
4 & WAMP & 89.23 & 84.56 & 83.09 & 83.21 \\ \hline
5 & SSC & 90.97 & 84.56 & 86.34 & 85.76 \\ \hline
6 & CPT4 & 94.45 & 88.77 & 84.55 & 90.9 \\ \hline
7 & Skewness & 97.57 & 92.63 & 88.84 & 92.32 \\ \hline
8 & Shape factor & 98.25 & 94.04 & 87.75 & 91.23 \\ \hline
9 & CPT3 & 97.57 & 93.68 & 88.15 & 88.32 \\ \hline
10 & Energy operator & 98.26 & 93.68 & 86.33 & 87.95 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Accuracy per class to CI_TL (time domain).