## 4 Experimental Result Analysis
From the experimental analysis, it is found that the LR and RF provide satisfactory results and SVM perform outstanding which makes our claim stronger for the proposed model. \begin{table}
\begin{tabular}{c c c c c c} \hline  & **No. of** & **No. of Writing for** & **Data Samples for One** & **Training and Feature** & **Test Data** \\  & **Keywords** & **Each Keywords** & **Person** & **Selection Data** & **Test Data** \\ \hline Class 1 & 10 & 5 & 50 & 30 & 20 \\ Class 2 & 10 & 5 & 50 & 30 & 20 \\ Class 3 & 10 & 5 & 50 & 30 & 20 \\... &... &... &... &... &... \\ Class 25 & 10 & 5 & 50 & 30 & 20 \\  & & & Total Data: 1250 & Total Train Data: 750 & Total Test Data: 500 \\ \hline \end{tabular}
\end{table} 
Table 4: Summary of Training and Testing Data Labeling. Figure 10 presents the classification accuracies of all sub-optimal feature vectors for 3 machine learning algorithms in the wrapper approach. From the 10 sub-optimal features, the wrapper approach identifies the best feature for higher classification results. Table 6 presents the best optimal feature sets and the classification accuracy for the selected best optimal feature set. Table 6 presents the best accuracy of finally selected best optimal features in wrapper approach using support vector machine, logistic regression, and random forest. From Table 6, we can see that SVM provides the best accuracy result compare to LR and RF. It is found that our proposed system provides a higher accuracy outcome with the combination of best optimal features {f\({}_{13}\), f\({}_{14}\), f\({}_{16}\), f\({}_{22}\)} with the best accuracy of 98.0% whereas the accuracy without feature selection is 93% using SVM. Using LR with the combination of best optimal features {f\({}_{6}\), f\({}_{9}\), f\({}_{23}\)} provides the best accuracy of 92.2% whereas the accuracy without feature selection is 86% only. Again, using RF with the combination of best optimal features {f\({}_{4}\), f\({}_{5}\), f\({}_{27}\)} provides the best accuracy of 94.6% whereas the accuracy without feature selection is 90%. These selected best features are then used in the validation process to check the overall accuracy of our proposed model of user identification. Our proposed  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multicolumn{10}{c}{**Sub-Optimal Feature Sets in Filter Approach**} \\ \hline
**1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** \\ \hline \{f_{2}\} & \({}_{5}\) & \({}_{7}\) & \({}_{8}\) & \({}_{9}\) & \({}_{1  
model provides efficient and satisfactory writer identification with limited computational resources and hardware cost which prove the practicality of our system. Figure 10: Classification accuracy of different sub-optimal feature sets in wrapper approach: (**a**) estimated by support vector machine (SVM) (**b**) estimated by logistic regression (LR) (**c**) estimated by random forest (RF). Figure 14: Confusion matrix of the proposed model for the twenty persons. Figure 12: Confusion matrix of the proposed model for the ten persons. Figure 13: Confusion matrix of the proposed model for the fifteen persons. The performance matrices, accuracy, precision, recall, and F1 score achieve higher values such as 98%, 98%, 98%, and 98% respectively when the number of persons is smaller. However, when the number of persons is 15, then the values of accuracy, precision, recall, and F1 score are 97.3%, 97.25%, 97.35%, and 87.3% respectively where the performance is changing insignificantly which is practical. Figure 17 presents the overall accuracy of different groups of persons with feature selection and without feature selection using the SVM linear kernel technique. From the experimental analysis, it is found that our proposed system achieves higher results with the feature selection method than no feature selection process. For SVM, the highest accuracy with feature selection is 98.0% where that of which is 92.20% with no feature selection technique.