## 3 Results and Reports
### Classification Performances  
#### 3.4.1 Initial Classification Results  
In order to validate the results obtained, it is necessary to compare the classification results using SVM with other classification algorithms. Therefore, four of the most commonly used classifiers in the field of learning were chosen, namely: linear discriminant analysis, k-nearest neighbors, XGBoost and random forest. Precision, recall, f1-score and accuracy were the standard metrics (scores) provided to judge the performance of our approach. We mainly chose the K-nearest neighbors (KNN) because of its non-parametric nature since it allowed the classifier to react promptly to input changes in real time, noting that it did not rely on data from a particular domain or a particular distribution (normal \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Class 0** & **Class 1** \\ \hline Class 0 & TP & FP \\ Class 1 & FP & FN \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Confusion matrix. \begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{1}{c}{**Performance Measures**} & **Formula** \\ \hline Accuracy & \(\frac{\text{TP}+\text{TN}+\text{FP}+\text{FN}}{\text{TP}+\text{FN}}\) \\ Precision & \(\frac{\text{TP}+\text{FP}}{\text{TP}}\) \\ Recall & \(\frac{\text{TP}+\text{FN}}{\text{TP}+\text{FN}}\) \\ F1-score & \(\frac{\text{2-TP}}{\text{2-TP}+\text{FN}+\text{FN}}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance of classification model assessors. distribution in our case). Since we also were working on a binary class model, the point clouds might be linearly separable in the representation space, choosing a linear separator such as the linear discriminant analysis (LDA) seemed to be adequate to our purpose. Based on a gradient boosting framework, XGBoost is a decision-tree-based classification algorithm that tends to outperform all machine learning algorithms when it comes to small and medium data. It uses several techniques such as parallel processing, tree-pruning and regularization to avoid overfitting. Another tree-based algorithm, named random forest (RF), was used during this comparison. RF can handle binary and numerical features with very low pre-processing, which needs to be done since data does not need to be rescaled or transformed. Therefore, comparing the results of our approach with those of XGBoost and RF would provide an excellent benchmark for the obtained performance. A set of experiments was conducted to provide a comparative study between these algorithms regarding their standard performances. This is shown in Table 6.  
The scores corresponding to classes 0 and 1 show the achieved results in classifying the data points in that particular class compared to the other one. The accuracy of the SVM (with RBF kernel) shows that the result obtained was very satisfactory given its values between 0.92 and 1 for all the standards. We could therefore conclude that our approach achieved the best performance for each dataset. In order to present a communicative result of our approach, it is also important to represent the calculations obtained during training and testing phases of learning. Table 7 illustrates the calculated accuracies of the three datasets. The related confusion matrices are represented in Table 8 to support the results achieved.