## 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 c} \hline \hline  & & \multicolumn{4}{c}{**Accuracy**} & \multicolumn{4}{c}{**Sensitivity**} & \multicolumn{4}{c}{**Specificity**} \\ \cline{2-11}  & & **Traditional** & **SimpleMI** & **WupperMI** & **Traditional** & **SimpleMI** & **WrapperMI** & **Traditional** & **SimpleMI** & **WrapperMI** \\ \hline \multirow{4}{*}{Trees methods} & DecisionStump & 0.677 & **0.860** & 0.730 & 0.458 & **0.949** & 0.935 & **0.822** & 0.703 & 0.300 \\  & J48 & 0.600 & **0.903** & 0.814 & 0.434 & 0.953 & 0.959 & 0.592 & 0.802 & 0.505 \\  & RandomTree & 0.036 & **0.865** & 0.792 & 0.504 & 0.889 & **0.951** & 0.616 & **0.807** & 0.463 \\  & RandomForest & 0.640 & **0.893** & 0.804 & 0.510 & 0.931 & **0.957** & 0.610 & **0.810** & 0.485 \\ \hline \multirow{4}{*}{Rules methods} & ZenR & 0.590 & **0.631** & **0.631** & 0.429 & **0.857** & **0.857** & **0.871** & 0.143 & 0.143 \\  & OneR & 0.676 & **0.898** & 0.692 & 0.458 & 0.966 & **0.958** & **0.818** & 0.770 & 0.156 \\  & Nipe & 0.674 & **0.878** & 0.672 & **0.954** & 0.905 & 0.891 & 0.397 & **0.814** & 0.298 \\  & PAKT & 0.607 & **0.903** & 0.817 & 0.443 & 0.949 & 0.957 & 0.604 & **0.806** & 0.515 \\  & Rider & **0.906** & 0.874 & 0.670 & 0.948 & **0.949** & 0.871 & **0.732** & 0.720 & 0.291 \\ \hline \multicolumn{11}{c}{} \\ NaiveBayes & 0.733 & **0.844** & 0.783 & 0.851 & 0.938 & **0.955** & 0.560 & **0.676** & 0.440 \\ \hline \multicolumn{11}{c}{} \\ Logistic & 0.798 & **0.876** & 0.785 & 0.822 & 0.945 & **0.962** & 0.716 & **0.738** & 0.417 \\ \hline \multirow{4}{*}{SVM methods} & LibSVM & 0.841 & **0.872** & 0.720 & 0.978 & 0.920 & **0.989** & _0.667_ & **0.769** & 0.218 \\  & SPSpasos & 0.480 & **0.870** & 0.697 & 0.548 & 0.948 & **0.964** & 0.483 & **0.717** & 0.193 \\  & SCD & 0.718 & **0.874** & 0.690 & 0.728 & 0.950 & **0.968** & 0.587 & **0.720** & 0.142 \\  & SMO & 0.719 & **0.874** & 0.767 & 0.722 & 0.953 & **0.965** & 0.585 & **0.716** & 0.354 \\ \hline \multirow{4}{*}{ANN methods} & RBFNetwork & 0.812 & **0.879** & 0.788 & 0.861 & 0.947 & **0.954** & 0.647 & **0.727** & 0.433 \\  & MultilayerPerceptron & 0.899 & **0.909** & 0.808 & 0.919 & 0.943 & **0.954** & 0.757 & 0.788 & 0.498 \\ \hline \multirow{4}{*}{Ensembles methods} & AdaBoostRandomForest & 0.725 & **0.892** & 0.798 & 0.677 & 0.930 & 0.953 & 0.719 & 0.810 & 0.478 \\  & AdaBoostTAPART & 0.696 & **0.899** & 0.816 & 0.684 & 0.946 & **0.956** & 0.617 & **0.799** & 0.514 \\  & AdaBoostKnaiveWes & 0.742 & 0.863 & 0.800 & 0.843 & 0.925 & **0.949** & 0.617 & 0.738 & 0.488 \\  & BaggingRandomForest & 0.642 & 0.906 & 0.808 & 0.513 & 0.958 & 0.957 & 0.610 & 0.799 & 0.495 \\  & BaggingRPART & 0.610 & 0.899 & 0.819 & 0.444 & 0.940 & **0.959** & 0.603 & 0.811 & 0.518 \\  & BaggingRAviewBayes & 0.735 & **0.836** & 0.775 & 0.849 & 0.945 & **0.958** & 0.525 & 0.648 & 0.416 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Average results by algorithm for all courses. The main conclusion extracted from this experimentation is the importance of an appropriate problem representation. It can be seen that the assignments represented with single instance learning obtain lower results. These results can explain why assignments are not widely included as influencing factors in previous studies of predicting students' performance. Using the same type of information and the same learning algorithms, but a representation based on MIL, algorithms can predict the student's success in distance education with more accuracy. Thus, we can see that flexible representation can obtain differences of more than 20% of performance in comparison with traditional representation.