## 4 Results and Discussion
### Correlation of Recognition Rate between the Classification Methods and Reduction of Dimension  
PCA was used for feature selection and to reduce the dimension of available attributes in the data set from 122 to 94. Classification was done by SKA, naive Bayes, J48, and the MLP algorithms through WEKA. Correlation of each kind of classification algorithm and dimension reduction was carried out, and Table 6 shows the correct rate of classification detection for each data set and the parameter settings. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \multicolumn{3}{c}{**Test Classification**} \\ \cline{2-5}  & \multicolumn{2}{c}{**First Set of Tests**} & **Second Set of Tests** & **Third Set of Tests** \\  & \multicolumn{2}{c}{**(Correct Rate/Time)**} & **(Correct Rate/Time)** & **(Correct Rate/Time)** \\ \hline \multirow{3}{*}{SVM-RBF} & Original data & 97.22\% & 82.34\% & 79.64\% \\  & (122 Features) & 2.12 (s) & 2.18 (s) & \\ \cline{2-5}  & Data after dimension & 96.4\% & 82.52\% & 79.72\% \\ \cline{2-5}  & reduction (94 Features) & 1.72 (s) & 1.76 (s) & 1.65 (s) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Detection accuracy of the classifiers of the different algorithms \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Type of Classifier**} & \multirow{2}{*}{**Data**} & \multicolumn{2}{c}{**First Setup (Correct Rate/Time)**} & **Second Group** & **Third Group** \\  & & **(Correct Rate/Time)** & **(Correct Rate/Time)** & **(Correct Rate/Time)** \\ \hline \multirow{3}{*}{MLP (N = 2)} & Original data & 97.76\% & 78.86\% & 76.06\% \\  & & 0.16 (s) & 0.16 (s) & 0.17 (s) \\ \cline{2-5}  & Data after dimension & 97.24\% & 80.78\% & 77.56\% \\  & reduction & 0.12 (s) & 0.19 (s) & 0.17 (s) \\ \hline \multirow{3}{*}{MLP (N = 4)} & Original data & 97.62\% & 85\% & 81.76\% \\  & Original data & 0.17 (s) & 0.17 (s) & 0.17 (s) \\ \cline{2-5}  & Data after dimension & 97.82\% & 84.56\% & 79.32\% \\  & reduction & 0.17 (s) & 0.17 (s) & 0.19 (s) \\ \hline \multicolumn{5}{c}{SA Training (Activation Functions: Soft Plus Loss Functions: Approximate Absolute Error—E 0.01)} \\ \hline \multirow{2}{*}{**Type of Classifier**} & \multirow{2}{*}{**Data**} & \multicolumn{2}{c}{**First Set of Tests**} & **Second Set of Tests** & **Third Set of Tests** \\  & & **(Correct Rate/Time)** & **(Correct Rate/Time)** & **(Correct Rate/Time)** \\ \hline \multirow{3}{*}{MLP (N = 2)} & Original data & 97.64\% & 79.92\% & 75.74\% \\  & & 0.17 (s) & 0.16 (s) & 0.17 (s) \\ \cline{2-5}  & Data after dimension & 94.38\% & 81.40\% & 77.26\% \\  & reduction & 0.27 (s) & 0.19 (s) & 0.16 (s) \\ \hline \multirow{3}{*}{MLP (N = 4)} & Original data & 95\% & 77.96\% & 72.18\% \\  & Original data & 0.19 (s) & 0.16 (s) & 0.27 (s) \\ \cline{2-5}  & Data after dimension & 94.56\% & 81.12\% & 75.98\% \\  & reduction & 0.14 (s) & 0.17 (s) & 0.14 (s) \\ \hline \multicolumn{5}{c}{SS training (Activation Functions: Soft Plus Loss Functions: Squared Error)} \\ \hline \multirow{3}{*}{MLP (N = 2)} & Original data & 97.52\% & 78.88\% & 76.68\% \\  & & 0.14 (s) & 0.17 (s) & 0.17 (s) \\ \cline{2-5}  & Data after dimension & 97.76\% & 79.90\% & 79.28\% \\  & reduction & 0.13 (s) & 0.23 (s) & 0.14 (s) \\ \hline \multirow{3}{*}{MLP (N = 4)} & Original data & 97.92\% & 78.92\% & 75.80\% \\  & & 0.16 (s) & 0.17 (s) & 0.16 (s) \\ \cline{1-1} \cline{2-5}  & Data after dimension & 97.52\% & 80.84\% & 77.34\% \\ \cline{1-1}  & reduction & 0.14 (s) & 0.19 (s) & 0.17 (s) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: _Cont._From the results, it can be clearly seen that the greater the amount of PCA dimension reduction, the faster the calculation speed. The reduction had no absolute correlation with the correct rate. When the classifier and parameter MLP-AS was N = 4, the same dimensionality reduction did not significantly improve the recognition rate. The MLP calculation speed was not improved either. However, its recognition rate was the highest among the classifiers, reaching 97.82%.