## 5 Experimentation and Results
### Comparative Study
#### 5.4.1 Comparative Analysis between Different Representations
\begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{AAA} & \multicolumn{4}{c}{BBB} & \multicolumn{4}{c}{CCC} \\ \cline{2-11}  & & \multicolumn{2}{c}{**Traditional**} & \multicolumn{2}{c}{**SimpleDiff**} & \multicolumn{2}{c}{**WaperMil**} & \multicolumn{2}{c}{**Traditional**} & \multicolumn{2}{c}{**SimpleDiff**} & \multicolumn{2}{c}{**Traditional**} & \multicolumn{2}{c}{**SimpleDiff**} & \multicolumn{2}{c}{**WaperMil**} \\ \hline \multirow{4}{*}{Trees methods} & DecisionStump & 0.710 & **0.918** & 0.789 & 0.655 & **0.823** & 0.627 & **0.794** & 0.788 & 0.740 \\  & J48 & 0.705 & **0.919** & 0.809 & 0.569 & **0.901** & 0.770 & 0.622 & **0.899** & 0.845 \\  & RandomTree & 0.693 & **0.866** & 0.796 & 0.624 & **0.853** & 0.750 & 0.701 & **0.860** & 0.832 \\  & RandomForest & 0.727 & **0.904** & 0.818 & 0.619 & **0.886** & 0.754 & 0.691 & **0.887** & 0.843 \\ \hline \multirow{4}{*}{Rules methods} & ZeroR & 0.710 & **0.752** & **0.752** & 0.525 & **0.618** & **0.618** & **0.622** & 0.509 & 0.509 \\  & OnR & 0.710 & 0.918 & 0.775 & 0.653 & **0.902** & 0.627 & 0.800 & **0.889** & 0.712 \\  & Ninge & **0.909** & 0.898 & 0.779 & 0.501 & **0.877** & 0.657 & 0.415 & **0.866** & 0.578 \\  & PART & 0.709 & **0.920** & 0.812 & 0.569 & **0.899** & 0.779 & 0.630 & **0.900** & 0.846 \\  & Rotor & 0.899 & **0.902** & 0.744 & **0.915** & 0.859 & 0.569 & **0.901** & 0.850 & 0.655 \\ \hline \multicolumn{11}{c}{} & NaiveBayes & 0.730 & **0.921** & 0.810 & **0.785** & 0.735 & 0.706 & **0.823** & 0.779 & 0.807 \\ \hline \multirow{4}{*}{SVM methods} & Logistic & 0.722 & **0.925** & 0.788 & 0.797 & **0.807** & 0.740 & **0.904** & 0.837 & 0.822 \\ \cline{2-11}  & LabSVM & 0.806 & **0.916** & 0.759 & **0.851** & 0.832 & 0.658 & 0.830 & **0.861** & 0.752 \\  & Spegasos & 0.301 & **0.913** & 0.759 & 0.489 & **0.795** & 0.627 & 0.454 & **0.824** & 0.662 \\  & SGD & 0.733 & **0.915** & 0.755 & 0.755 & **0.805** & 0.627 & 0.815 & **0.836** & 0.729 \\  & SMO & 0.721 & **0.918** & 0.759 & 0.741 & **0.808** & 0.735 & 0.806 & **0.837** & 0.815 \\ \hline \multirow{2}{*}{ANN methods} & RBPNetwork & 0.758 & **0.859** & 0.813 & 0.856 & **0.907** & 0.687 & **0.882** & 0.873 & 0.829 \\  & MultilayerPerceptron & 0.879 & **0.8919** & 0.808 & 0.911 & **0.939** & 0.760 & **0.918** & 0.907 & 0.837 \\ \hline \multirow{4}{*}{Ensembles methods} & AdaBoostRRandomForest & 0.656 & **0.905** & 0.813 & 0.743 & **0.885** & 0.754 & 0.689 & **0.884** & 0.838 \\  & AdaBoostR*PART & 0.738 & **0.918** & 0.811 & 0.699 & **0.894** & 0.778 & 0.715 & **0.894** & 0.847 \\  & AdaBoostR*NaiveBayes & 0.730 & **0.921** & 0.809 & 0.790 & **0.805** & 0.753 & **0.836** & 0.788 & 0.830 \\  & Bagging+RandomForest & 0.726 & **0.919** & 0.820 & 0.625 & **0.905** & 0.760 & 0.690 & **0.903** & 0.845 \\  & Bagging+PART & 0.715 & **0.915** & 0.811 & 0.562 & **0.895** & 0.778 & 0.629 & **0.892** & 0.848 \\  & Speging+KnaveBayes & 0.732 & **0.920** & 0.809 & **0.785** & 0.699 & 0.683 & **0.826** & 0.777 & 0.795 \\ \hline \end{tabular}
\end{table} 
Table 10: Accuracy results by algorithm and courses I/II.