# Confidence-Based Voting for the Design of Interpretable Ensembles with Fuzzy Systems +
## 3 Results

Table 6: Datasets generated from the vital sensing experiments. Figure 7: CBV accuracy dependence on the threshold value, random forest as assisting classifier. * The \"Listened\" problem, in which an instance was labelled as \"1\", listened to music, and \"0\" otherwise;
* The \"Liked\" problem, in which an instance was labelled as \"1\", liked the music, and \"0\" otherwise. The parameters of the HEFCA algorithm were changed to match the properties of the collected dataset, in particular the number of generations was set to 500. The neural network architecture was changed, i.e., it had only 20 nodes in a single hidden layer, and the number of epochs for training was set to 5. Same as for the test datasets, accuracy, F-measure and confusion matrix were estimated. The results of the experiments are presented in Tables 7 and 8. For the first problem \"Liked\" the CBV did not deliver any accuracy improvements, probably due to the fact that the accuracy is too low. This could happen because of the small size of the available training set. The second problem, \"Listened\", was classified better, i.e., the neural network had much better accuracy, but the F-measure was equal to 0, which shows that the neural network actually did not recognize one of the classes, while the HEFCA generated classifier did it. The combination of these methods with CBV resulted in increased F-measure values for the second class, but the F-measure on the first class has dropped. In the next two Tables 9 and 10, the confusion matrixes are presented for both \"Liked\" and \"Listened\" problems. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **HEFCA** & **NN (Keras)** & **CBV** \\ \hline Liked & 0.616 & 0.550 & 0.600 \\ Listened & 0.613 & 0.750 & 0.688 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Accuracies of HEFCA, neural network and CBV on real-world problems. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & \multicolumn{2}{c}{**HEFCA**} & \multicolumn{2}{c}{**NN (Keras)**} & \multicolumn{2}{c}{**CBV**} \\ \hline  & Class 1 & Class 2 & Class 1 & Class 2 & Class 1 & Class 2 \\ Liked & 0.535 & 0.465 & 0.419 & 0.482 & 0.539 & 0.482 \\ Listened & 0.220 & 0.784 & 0.000 & 0.857 & 0.155 & 0.823 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: F-measures of HEFCA, neural network and CBV on real-world problems. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**HEFCA** & \multicolumn{4}{c}{**Actual**} \\ \hline  & Class 1 & Class 2 & Reject \\
**Predicted** & 2.1 & 0.9 & 0 \\  & 1.4 & 1.6 & 0 \\ \hline
**NN (Keras)** & \multicolumn{4}{c}{**Actual**} \\ \hline  & Class 1 & Class 2 & Reject \\
**Predicted** & 1.6 & 1.4 & 0 \\  & 1.3 & 1.7 & 0 \\ \hline
**CBV** & \multicolumn{4}{c}{**Actual**} \\ \hline  & Class 1 & Class 2 & Reject \\
**Predicted** & 1.9 & 1.1 & 0 \\  & 1.3 & 1.7 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Confusion matrixes for the “Liked” problem. The confidence-based voting procedure has allowed improving the quality of classification, in particular, the second class was recognized much better. For the \"Listened\" problem, the accuracy was also improved for the second class, the reason for this is that the neural network classified all instances to the second class. Still, with CBV the accuracy of the fuzzy classifier was improved.