## 5 Experimental Results
#### Performance Results  
The performance results for the air quality dataset show that the training data are divided into four fragments: Fragment 1 contains 459 samples, Fragment 2 has 414 samples, Fragment 3 has 163 samples, and Fragment 4 has 440 samples. These fragments are then allocated to different nodes for processing. Node 0 is assigned Fragment 1, Node 1 is assigned Fragment 2, Node 2 is assigned Fragment 3, and Node 3 is assigned Fragment 4. Similarly, the testing data are also divided into four fragments. Fragment 1 contains136 samples, Fragment 2 has 101 samples, Fragment 3 has 32 samples, and Fragment 4 has 100 samples. These testing fragments are allocated to the same nodes as the training data fragments. Node 0 is assigned Fragment 1, Node 1 is assigned Fragment 2, Node 2 is assigned Fragment 3, and Node 3 is assigned Fragment 4. The fragment allocations ensure that the data are distributed across different nodes for efficient analysis. The allocation of data fragments to specific nodes is an important step in optimizing the performance of the data fragmentation and allocation process. The allocation of fragments with training and testing data are provided in Table 4.  
The performance results for the wine quality dataset reveal that the training data are fragmented into six distinct fragments. Fragment 1 contains 14 samples, Fragment 2 has 136 samples, Fragment 3 has 1171 samples, Fragment 4 has 1744 samples, Fragment 5 has 709 samples, and Fragment 6 has 140 samples. These fragments are then allocated to different nodes for processing. Node 0 is assigned Fragment 1, Node 1 is assigned Fragment 2, Node 2 is assigned Fragment 3, Node 3 is assigned Fragment 4, Node 4 is assigned Fragment 5, and Node 5 is assigned Fragment 6. Similarly, the testing data are divided into six fragments as well. Fragment 1 contains 6 samples, Fragment 2 has 27 samples, Fragment 3 has 286 samples, Fragment 4 has 454 samples, Fragment 5 has 171 samples, and Fragment 6 has 35 samples. These testing fragments are allocated to the same nodes as the training data fragments. Node 0 is assigned Fragment 1, Node 1 is assigned Fragment 2, Node 2 is assigned Fragment 3, Node 3 is assigned Fragment 4, Node 4 is assigned Fragment 5, and Node 5 is assigned Fragment 6. The fragmentation and allocation process ensure that the data are efficiently distributed across multiple nodes for effective analysis. The overall performance and scalability of the data fragmentation approach are enhanced by dividing the data into smaller fragments and allocating them to different nodes. The performance evaluation of the RFO-SVM algorithm for data fragmentation and allocation is shown in Table 5. The results are obtained for 50 iterations of the algorithm for both the air quality dataset and the wine quality dataset. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{4}{c}{**Air Quality Data**} \\ \hline
**Fragments** & **Training** & **Testing** & **Final Allocation** \\ \hline
1 & 459 & 136 & 0 \\ \hline
2 & 414 & 101 & 1 \\ \hline
3 & 163 & 32 & 2 \\ \hline
4 & 440 & 100 & 3 \\ \hline \multicolumn{4}{c}{Wine Quality Data} \\ \hline Fragments & Training & Testing & Final Allocation \\ \hline
1 & 14 & 6 & 0 \\ \hline
2 & 136 & 27 & 1 \\ \hline
3 & 1171 & 286 & 2 \\ \hline
4 & 1744 & 454 & 3 \\ \hline
5 & 709 & 171 & 4 \\ \hline
6 & 140 & 35 & 5 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Data Fragmentation Results. For the air quality dataset, the evaluation metrics demonstrate a high-level accuracy of 96.21%, and a precision of 96.23%, indicating a high proportion of correctly predicted positive instances out of the total positive predictions. The recall rate is 95.65%, and the F1 score, which combines precision and recall, is calculated at 95.93%, providing a balanced measure of the model's performance. The allocation cost per fragment for the air quality dataset is measured at 801.18 Mbps, indicating the computational resources required for data fragmentation.