## 5 Experiments and Results
### Classification Precision Metric Test  
Regarding the comparison of the classification algorithms on the classification precision metric, the six classification algorithms were applied to the three artificial datasets. For each algorithm, we used the parameters delimited by Table 6 and the test with predictor model cross-validation equal to a value of 10. Therefore, the algorithms were executed 30 times in each artificial dataset. For each run, we used the random seed generator with different values following an increasing pattern, starting from the value 1. This was done to obtain greater randomness in the metric results, and for the Friedman test Section 5.5. Therefore, each result of the classification accuracy metric for each seed was placed in an Excel spreadsheet to calculate the average of the metric between the algorithms. Unlike the other tests performed, the classification algorithms were executed in the NetBeans programming IDE using the WEKA package that contains the main features of the tool. This was necessary due to a large number of iterations performed in each algorithm, as it would take a long time if it were performed in GUI technology. Table 9 presents the average results of each randomization seed from the classification accuracy metric. From Table 9, we observed that all algorithms obtained the metric value above 70% in all artificial datasets. Analyzing the comparison of the algorithms separately in each dataset, in Target, almost all the algorithms obtained the percentage with the maximum value, only the KNN algorithm obtained a lower value. In the Period dataset, the MLP algorithm obtained the best result, the PRISM, J48, and KNN algorithms obtained similar values close to the MLP algorithm value, and the NB and SVM algorithms obtained lower values than the other algorithms. In Setting, the same behavior of the previous dataset was maintained, being the MLP algorithm with the best value, the PRISM, J48, and KNN algorithms with values similar to the MLP value, and finally the NB and SVM algorithms with lower values than other algorithms. Upon considering the comparison made of the classification algorithms on the artificial datasets and analyzing it in general, it was observed that the MLP algorithm, in all sets, obtained the best \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Artificial Dataset** & **NB** & **PRISM** & **J48** & **KNN** & **SVM** & **MLP** \\ \hline
**Target** & 0.47 ms & 12.03 ms & 2.19 ms & 0.47 ms & 43.44 ms & 15.815,31 ms \\
**Period** & 0.47 ms & 124.69 ms & 5.63 ms & 0.47 ms & 417.66 ms & 15.969,53 ms \\
**Setting** & 0.94 ms & 48.91 ms & 3.75 ms & 0.16 ms & 472.66 ms & 14.249,53 ms \\
**Average** & 0.62 ms & 61.87 ms & 3.85 ms & 0.36 ms & 311.25 ms & 62.011,45 ms \\ \hline \hline \end{tabular}
\end{table} 
Table 7: CPU time test for training. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Artificial Dataset** & **NB** & **PRISM** & **J48** & **KNN** & **SVM** & **MLP** \\ \hline
**Target** & 1.25 ms & 0.16 ms & 0.63 ms & 116.56 ms & 6.25 ms & 2.19 ms \\
**Period** & 1.56 ms & 1.87 ms & 0.31 ms & 123.59 ms & 47.03 ms & 3.13 ms \\
**Setting** & 1.72 ms & 0.47 ms & 0.31 ms & 123.28 ms & 49.06 ms & 1.25 ms \\
**Average** & 1.51 ms & 0.83 ms & 0.41 ms & 121.14 ms & 34.11 ms & 2.19 ms \\ \hline \hline \end{tabular}
\end{table} 
Table 8: CPU time test for classification. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Artificial Dataset** & **NB** & **PRISM** & **J48** & **KNN** & **SVM** & **MLP** \\ \hline
**Target** & 100\% & 100\% & 100\% & 99.99\% & 100\% & 100\% \\
**Period** & 80\% & 96.87\% & 97.49\% & 97.22\% & 87.53\% & 99.96\% \\
**Setting** & 85.26\% & 98.86\% & 99.54\% & 99.89\% & 86.89\% & 100\% \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Comparison of classification accuracy metric. classification performance when compared to other algorithms, following the PRISM, J48, and KNN algorithms whose performances were very close to the MLP algorithm.