## 6 Conclusions and Future Works  
The balance accuracy and F1 measure values shown in Table 3 illustrate the superiority exhibited by the Naive Associative Classifier with respect to eight of the most relevant computer algorithms of the state of the art, when performing the task of intelligent classification of tourist service clients, according to their non-verbal preferences. However, these values are not very satisfactory, which is why the proposal of this research work, the Customized Naive Associative Classifier, arises through modifications to the Naive Associative Classifier. According to Table 4, the proposed algorithm achieved the best values of balanced accuracy and F1 measure with respect to the nine algorithms included in Table 3. The main limitation of our proposal is that, although the best performance is obtained when comparing with nine of the best models in the state of the art, the values obtained are not good enough for the tourism industry to adopt our model as a standard. The authors are convinced that it is possible to improve these results, and one way that we have discussed in our research group is by searching for new modifications to the CNAC, and by incorporating concepts from other Machine Learning novel approaches such as the recently published Minimalist Machine Learning paradigm. These tasks remain as future works.  
Conceptualization, C.C.T.-R. and Y.V.-R.; methodology, R.T.-P.; validation, R.T.-P., C.C.T.-R. and Y.V.-R.; formal analysis, O.C.-N.; investigation, C.Y.-M.; data curation, C.C.T.-R.; Writing--Original draft preparation, Y.V.-R.; Writing--Review and editing, C.Y.-M.; visualization, Y.V.-R.; supervision, R.T.-P. and C.Y.-M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
All clients responding the questionnaire give their verbal consent to the use of their information, due to the questionnaire is anonymous, and no personal data was gathered.  
The data of the questionnaire was upload as supplementary material of the paper and will be donated to the Machine Learning Repository of the University of California at Irvine.  
The authors would like to thank the Instituto Politecnico Nacional (Secretaria Academica, Comision de Operacion y Fomento de Actividades Academicas, Secretaria de Investigacion y Posgrado, Escuela Superior de Turismo, Centro de Investigacion en Computacion, and Centro de Innovacion y Desarrollo Tecnologico en Computo), the Consejo Nacional de Ciencia y Tecnologia, and Sistema Nacional de Investigadores for their economic support to develop this work.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithm** & \begin{tabular}{c} **User-Provided** \\ **Similarity Function** \\ \end{tabular} & \
\begin{tabular}{c} **Embedded Feature** \\ **Selection** \\ \end{tabular} &
\begin{tabular}{c} **Embedded** \\ **Feature Weighting** \\ \end{tabular} & \multicolumn{2}{c}{**Complexity 1**} \\ \cline{1-1}  & & & & **Training 2** & **Testing 3** \\ \hline ACID & Yes & Yes & Yes & Polynomial & Sub-linear 4 \\ ALVOT & Yes & Yes & Yes & Non-polynomial & Linear \\ C4.5 & No & Yes & No & Polynomial & Sub-linear 4 \\ CNAC & Yes & No & Yes & Polynomial & Linear \\ EG & No & Yes & Yes & Polynomial & Linear \\ NAC & **No** & **No** & Yes & Polynomial & Linear \\ NB & No & No & No & Linear & Sub-linear 4 \\ NN (k = 1) & Yes & No & **No** & Unitary & Linear \\ NN (k = 3) & Yes & No & **No** & Unitary & Linear \\ RIPPER & No & Yes & No & Polynomial & Sub-linear \\ \hline \hline \end{tabular}  
* We just consider computational complexity, not storage complexity. 2 With respect to the number of features. 3 With respect to the number of instances. 4 Considering the number of instances much greater than the number of features.  
\end{table}
Table 5: Qualitative comparison of the proposed CNAC vs. the compared supervised classifiers.