# Driving-Style Assessment from a Motion Sickness Perspective Based on Machine Learning Techniques
## 5 Experimental Results of the Applied Methodology  
### Data Processing Algorithms  
First, in order to clean errors in the dataset, the local outlier factor algorithm was selected, eliminating 25 samples. It was observed that the isolation forest method had a very aggressive approach to data point elimination, which with our limited sample size brought problems down the line.  
Concerning dimensionality reduction, first, the Pearson coefficient for correlation analysis was analyzed. In Figure 6, all variables presented in Section 3, are compared to the motion sickness criteria: MSDVxy. Secondly, recursive feature elimination (RFE) with a random forest regressor was applied to all variables until five variables were left. The results of both methods are summarized in Table 1.  
In the first group, all variables with a higher factor than 0.5 to MSDVxy were declared of interest. In the second, only the variables which showed a correlation higher than 0.67 were selected. The third group contains the five variables obtained from applying the RFE algorithm. From the three options, the options with fewer variables are preferable, so, the two options that contained five variables from Table 1 were selected. Moreover, SWA showed a 0.9 correlation with a SWRS variable; thus, among the two five-variable options, the variables obtained with RFE were selected.  
Concerning PCA technique results, the starting seven driver-controlled variables were transformed into five new PCA variables (PCA\({}_{1}\), PCA\({}_{2}\), PCA\({}_{3}\), PCA\({}_{4}\), PCA\({}_{5}\)). These five  
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{1}{c}{**Variables**} \\ \hline
**CA (Coeff \textgreater{} 0.5)** & _ACCy, YR, SWRS, ACCx, SWA,_ \\  & _BP, ACCx\_neg, PR, RR_ \\ \hline
**CA (Coeff \textgreater{} 0.67)** & _ACCy, YR, SWRS, ACCx, SWA_ \\ \hline
**RFE (N = 5)** & _ACCy, YR, SWRS, ACCx, ACCx\_neg_ \\ \hline \hline \end{tabular}
\end{table}
Table 1: Different groups of variables after applying feature selection techniques.  
Figure 6: Correlations between all variables that belong to accelerations, rotational speeds, driver—controlled variables, and our discomfort criteria (MSDVxy).  
variables are enough to explain 95% of the variance of the original dataset. The way that PCA functions is that it finds the direction with the highest percentage (%) of the variance of the data space, thus, PCA1 explains over 35% of the total variance, and PCA2, the second direction with the most variance, explains almost 25%.  
The new dimensions of our PCA-created space are a combination of the initial variables, orthogonal, and they minimize the reconstruction error of the samples. Each new variable can be decomposed as a linear combination of the starting parameter used to train the following Equation (4). Coefficients for the PCA model can be found in Table 2. Since we can deconstruct the newly created axis, we can still maintain the high interpretability of the variables.  
\[PCA^{i}=\beta_{1}^{i}X_{1}+\beta_{2}^{i}X_{2}+\cdots+\beta_{N}^{i}X_{N} \tag{4}\]  
Regarding oversampling, after considering different possibilities, MSDVxy was divided into five equidistant categories, each representing a higher motion sickness sensation ranging from very low to very high as seen in Table 3. It is obvious that the MSDVxy distribution over all samples is not regular since there are many more samples with low MSDVxy than high MSDVxy.  
The three algorithms (SMOTE, BorderlineSmote, and SVMSmote) perform their tasks of generating high-motion sickness samples adequately, balancing the number of samples in each category. As a result, all categories had 432 samples after the algorithms were applied. It was concluded that the best results were achieved with BorderlineSmote and SVMSmote algorithms, with almost no difference between them. Applying any of those oversampling algorithms showed big improvements over not applying oversampling algorithms at all.