## 5 Results and Industrial Interpretation
### Classification: State and Mode Prediction
Classifiers based on the optimization of a complex function, such as Neural Networks or Radial Basis Function SVM, do not obtain competitive results despite the computational cost involved in the parameter optimization process. 4. All the top ranked methods are ensembles, which hardly vary from each other in their performance. 5. kNN is the best of the non-ensemble methods. It is once again due to the low number of characteristics (i.e., there is no curse of dimensionality) and the high number of instances. Conclusions 2 and 3 point out the complexity of the decision boundaries for both problems. Tables 4 and 5 repeat the same structure as Table 3, but using F-Micro and MCC. In Table 4, the best F-Micro for State is achieved by LogitBoost (i.e., not by the best ranked method), although we checked that \"*\"s remained the same in the State column, if the methods were compared to LogitBoost. However, in Table 5, the best MCC value for the Mode variable is achieved by the second ranked method (Bagging of Random Balance of C4.5). In this case, when the methods were compared to this Bagging variant, every other method would be tagged with \"*\". So, when MCC was chosen as a metric, Bagging of Random Balance of C4.5 was significantly better than all the other methods for the Mode variable. Some additional conclusions that could be drawn from these two tables are:  
1. The C4.5 tree reached very similar values to those of some ensembles. Usually, it is expected that the variance component of the classification error decreases as the training dataset increases [54]. It is known that Bagging-based ensembles reduced this error component, as Boosting-based ensembles also do in their later iterations [55]. Hence, these similar results of C4.5 vs. ensembles can be explained, at least in part, by the dataset size and the superiority of decision trees over the other non-ensemble methods. 2. It is also interesting to note that the F-Micro and MCC for State variable prediction almost reaches one (i.e., the maximum possible value) in many of the classifiers that were tested, and in nearly all in the case of F-Micro. This points out again that the State is well characterized by the attributes of this data set. 3. However, the Mode variable figures are worse. For both F-Macro and MCC metrics, Random Balance Bagging, which is a specific method for imbalanced datasets, is the best choice. The \"+\" sign in \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(\Delta\) & **V** & **D** & **Method** & **State** & **Mode** \\ \hline
20 & 20 & 0 & Rotation Forest of C4.5 & 0.99756 & **0.94374** \\
13 & 16 & 3 & Bagging of C4.5 & 0.99711 * & 0.94292 \\
12 & 15 & 3 & LogitBoost of REPTree & **0.99759** & 0.94125 * \\
11 & 14 & 3 & AdaBoost.M1 of C4.5 & 0.99755 & 0.99346 * \\
10 & 15 & 5 & C 4.5 & 0.99696 * & 0.94307 * \\
10 & 13 & 3 & Rotation Forest of Random Forest & 0.99752 & 0.94007 * \\
3 & 10 & 7 & Random Forest & 0.99710 * & 0.94029 * \\
1 & 10 & 9 & Bagging de Random Balance of C4.5 & 0.99664 * & 0.93855 * \\ \(-8\) & 8 & 16 & kNN & 0.99198 * & 0.93582 * \\ \(-12\) & 6 & 18 & Multilayer Perceptron & 0.93112 * & 0.90334 * \\ \(-16\) & 4 & 20 & Radial Basis Function SVM & 0.88398 * & 0.88322 * \\ \(-22\) & 1 & 23 & Linear SVM & 0.80947 * & 0.71559 * \\ \(-22\) & 1 & 23 & Naive Bayes & 0.81845 * & 0.51293 * \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Ranking of F-Micro in the classification problems. V is the number of times the method has a better statistically significant F-Micro when compared with the other 12 methods through the 2 predicted variables. D is the number of times the method has a worse statistically significant F-Micro when compared with the other 12 methods through the 2 predicted variables. The ordering criteria \(\Delta\), is equal to V – D. The columns State and Mode have the average F-Micro figures for the method using \(10\times 10\)CV. (*) represents this method has an average F-Micro that is significantly worse than the one for the best-ranked method.