## 4 Results
### Standalone M3 GP  
The main reason for using M3 GP was not to obtain a classifier with superior results to the SOA algorithms, but to obtain a feature transformation that could improve the results obtained by the SOA algorithms.  
M3 GP was run with the settings shown in Table 4 with the spectral feature (MFCC, ARRC, and SPEC) arrangement of the BED dataset. The best results, presented in Table 5, were not significantly different from the SOA methods shown in Table 6. There are many reasons for the low performance of well-known algorithms (e.g., the number of features is too high, there are not enough samples, there is an unbalanced dataset) and when considering all possibilities, users may be tempted to apply some form of data treatment, but not letting the evolutionary process use all available data can affect the performance of the evolutionary algorithms by removing the possibility of letting the natural selection process find the ideal treatment.  
Figure 14: The confusion matrix of the training stages of the reported results obtained after the M3 GP transformation for different neural networks (Relu, Tanh and Logistic).  
Figure 13: Training and testing validations scores for the DL after M3 GP (ReLU function).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Classifier** & **Average Performance** \\ \hline Neural Network [31; 32; 33] & 85.80\% \\ Support Vector Machine [34; 35; 36] & 77.80\% \\ K-Nearest Neighbor [33; 37; 38] & 88.94\% \\ Multi-layer Perceptron [39; 40; 38] & 78.16\% \\ Bayes [41; 42; 43] & 69.62\% \\ Extreme Learning Machine [41] & 87.10\% \\ K-Means [43] & 78.06\% \\ Linear Discriminant Analysis [42] & 71.30\% \\ Gaussian Process [44] & 71.30\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Performance comparisons between SOA techniques reported in [30]. Similar EEG-based works have reported an overall recognition rate of 82.9%.  
\begin{table}
\begin{tabular}{c c c} \hline \hline Runs & 10 \\ Population Size & 500 individuals \\ Generations & 100 generations \\ Initial Dimension & 10 dimensions \\ Initialization & 6-depth full initialization [21] \\ Data & 70\% Training \(-\)30\% Tests \\ Operator Probabilities & Crossover \(p_{c}=0.5\), Mutation \(p_{\mu}=0.5\) \\  & \(+(plus),-(minus),\times(times)\), \(\div\) protected as in [21], sine, \\ Function Set & cosine, exponential, logarithm (abs), power\({}^{2}\), power\({}^{3}\), square \\  & root (postive), tangent (tan), hyperbolic tangent (tanh) \\ Terminal Set & Ephemeral random constants \([0,1]\) \\ Bloat Control & 17-depth limit [21] \\ Selection & Lexicographic tournament [29] of size 5 \\ Elitism & Keep best individual \\ \hline \hline \end{tabular}
\end{table}
Table 4: M3 GP setup settings.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Function** & **AUC \%** & **CA \%** & **F1 \%** & **Precision \%** \\ \hline
**ReLU** & 11.1 & 27 & 26.9 & 26 \\ \hline
**Tanh** & 9.2 & 20.4 & 20.4 & 20.8 \\ \hline
**Logistic** & \(-5.6\) & \(-2.1\) & \(-5.2\) & \(-2.5\) \\ \hline \hline \end{tabular}
\end{table}
Table 7: The percentage of classification improvement.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**M3 GP** & **MFCC** & **ARRC** & **SPEC** \\ \hline
**Train** & 94.4 & 59.9 & 46.3 \\ \hline
**Test** & 39.1 & 39.6 & 40 \\ \hline
**Root Node Features** & 63 & 23 & 13 \\ \hline
**Original Features** & 168 & 168 & 224 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The accuracy obtained by M3 GP, the number of root node features of the best solution found in each dataset, and the original features of each set.