## 4 Results and Discussion
### SVM Classifier Results  
The SVM model has two very important parameters, C and gamma. Where C is the penalty factor, which is the tolerance for error. The higher the value of c, the less the tolerance and over-fitting is easy. The smaller the value of C, the easier it is to fit. If C is too big or too small, the generalization ability will suffer and become worse. Gamma is a parameter that comes with the function after selection of the RBF function as the kernel. It implicitly determines the distribution of data after mapping to a new feature space. The larger the gamma value, the smaller the support vector, the smaller the gamma, the larger the support vector. The number of support vectors affects the speed of training and prediction. In the process, we experimented with the parameters c and g settings, and used the parameter check program grid.py to find the best parameters c and g. After the program is executed,  
Figure 1: Data sampling diagram.  
the last set of parameters is 0.03125, 0.0078125, and 91.9657 (Figure 2), where c = 0.03125, g = 0.0078125, the parameters c and g are brought into the SVM classifier respectively, the correct rate of training and test results are 92.2396% and 67.7032% respectively. After several parameter adjustment experiments, it was decided to use c = 1.0 and g = 0.0 as the best parameter values to verify with the other three classifiers in the paper.  
Weka was used for pre-processing. Feature selection, dimension reduction to 94 features, and training and testing of the data set was done as before and according to the status of each data set. The kernel function (parameter setting: parameter gamma = 0.0, parameter C = 1.0) was applied including linear SVM, polynomial, RBF, and sigmoid for training and prediction, and the results are shown in Table 2. It can be seen that the test of the first group had the best original data recognition. When used for recognition, the kernel of the RBF can reach 97.22%. The linear kernel recognition rate for the first group, after dimensionality reduction, can reach 96.68%. Although the SVM classifier showed no enhancement of recognition rate after dimension reduction, the calculation speed was substantially improved.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Type of Classifier**} & \multirow{2}{*}{**Data**} & **First Group** & **Second Group** & **Third Group** \\  & & **(Correct Rate/Time)** & **(Correct Rate/Time)** & **(Correct Rate/Time)** \\ \hline \multirow{4}{*}{SVM (Linear)} & Original data & 97.38\% & 81.74\% & 78.78\% \\  & Original data & 0.83 (s) & 0.56 (s) & 0.81 (s) \\ \cline{2-5}  & Data after Dimension & 96.68\% & 83.22\% & 80.72\% \\  & Reduction & 0.81 (s) & 0.78 (s) & 0.78 (s) \\ \hline \multirow{4}{*}{SVM (Polynomial)} & Original data & 96.36\% & 77.12\% & 73.00\% \\  & Original data & 2.23 (s) & 2.2 (s) & 2.64 (s) \\ \cline{2-5}  & Data after Dimension & 93.58\% & 73.48\% & 69.82\% \\  & Reduction & 6.07 (s) & 5.13 (s) & 5.16 (s) \\ \hline \multirow{4}{*}{SVM (RBF)} & Original data & 97.22\% & 82.34\% & 79.64\% \\  & Original data & 2.12 (s) & 2.18 (s) & 2.06 (s) \\ \cline{2-5}  & Data after Dimension & 96.40\% & 82.52\% & 79.72\% \\  & Reduction & 1.72 (s) & 1.76 (s) & 1.65 (s) \\ \hline \multirow{4}{*}{SVM (Sigmoid)} & Original data & 94.46\% & 82.78\% & 78.32\% \\  & Original data & 1.04 (s) & 0.97 (s) & 1.47 (s) \\ \cline{1-1} \cline{2-5}  & Data after Dimension & 95.20\% & 82.88\% & 78.82\% \\ \cline{1-1}  & Reduction & 1.22 (s) & 1.64 (s) & 1.25 (s) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Training and detection results for each SVM kernel function  
Figure 2: Optimal parameters c and g.