## 5 Supervised Classification of Clients
### Execution of State-of-the-Art Algorithms
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & **Balanced Accuracy** & **Averaged F1** & **Training Time 1** & **Testing Time 1** \\ \hline ACID & 0.5316 & 0.5104 & 0.4922 & 0.0020 \\ ALVOT & 0.6968 & 0.6502 & 2.4162 & 0.4711 \\ C4.5 & 0.4027 & 0.4006 & 0.0658 & 0.0002 \\ EG & 0.5125 & 0.4772 & 0.6778 & 0.0020 \\ NAC & **0.7181** & **0.6747** & 5.3552 & **0.0001** \\ NB & 0.6783 & 0.6549 & 3.1702 & 0.0020 \\ NN (k = 1) &mended. This is the reason why it is adopted in this study, given the obvious advantages it exhibits. The positive effects on the overall results are obvious. Because we are dealing with multiclass imbalanced data, to test the performance of the algorithms we used a multiclass confusion matrix (Figure 5). We compute two measures of performance: the balanced accuracy and the averaged F1 measure. Such measures are robust in presence of imbalanced data and can be easily obtained from a multiclass confusion matrix. The most important measure of performance of supervised classifiers is accuracy. However, accuracy privileges the majority class, and the results it produces have a strong bias towards the majority class, thereby disregarding the minority class. In order to take into account the two types of classes, the majority and the minority, the specialized literature [70] strongly recommends the balanced accuracy and the averaged F1 measure, which are adopted as measures of performance in our research, given the obvious advantages it exhibits. The positive effects on the overall results are obvious. \[Balanced\ Accuracy=\frac{1}{k}\sum_{i=1}^{k}\frac{n_{ii}}{\sum_{j=1}^{k}n_{ij}} \tag{8}\]  
\[\frac{1}{F1}=\frac{1}{k}\sum_{i=1}^{k}\frac{2*\frac{n_{ii}}{\sum_{j=1}^{k}n_{ ji}}*\frac{n_{ii}}{\sum_{j=1}^{k}n_{ij}}}{\frac{n_{ii}}{\sum_{j=1}^{k}n_{ij}}+ \frac{n_{ii}}{\sum_{j=1}^{k}n_{ij}}} \tag{9}\]  
In a classification problem with \(k\) classes, the balanced accuracy takes into consideration the total of correctly classified instances from each class, relative to the total of instances of such class. The averaged F1 measure considers both precision and recall for each of the classes. These performance measure allow us to evaluate the global performance of classification algorithms over all the classes in the problem, without bias towards majority class. The experiments carried show different performance values of the algorithms (Table 3), according to the balanced accuracy and averaged F1 measure. As can be seen, the best result was obtained by the NAC algorithm, with a balanced accuracy value of 0.7181 and an averaged F1 measure value of 0.6747. Regarding the execution times, the fastest algorithm for training is Nearest Neighbor, while for testing are RIPPER and NAC classifiers. In general, all algorithms are fast, due to the total time of the slowest algorithm do not surpass the six seconds, considering the whole validation procedure. Considering the above-mentioned results, such performance values aren't good enough to be deployed in a real-time eCRM, inside a hotel facility. Thus, we introduce a novel classification algorithm, based on the best-performing one, the NAC classifier, to improve the classification of the non-verbal preferences of the clients. In order to determine the preference of a new customer, it is necessary to apply the survey, and subsequently, with said data, use a supervised classification algorithm capable of determining what type of preference they belong to. The No Free Lunch theorems [46] hold that there is no superiority of one classifier over others, over all data sets and all performance measures. However, it is possible to analyze the performance of the classifiers in some scenarios. Figure 5: Confusion matrix for a \(k\) classes problem.