## 4 Conclusions  
In this paper, we proposed a new neural architecture for object classification, whose size in terms of the number of layers and neurons is automatically determined from the learning examples. Each layer of this architecture is a competitive neural network, whose output neurons represent class prototypes that can be used as a basis for the nearest prototype decision rule in order to predict the output labels for new examples. The training process of this classification model is performed using a combination of unsupervised and supervised modes. The experimental results obtained for a collection of 13 well-known public benchmark datasets show that the proposed model has good robustness in classification tasks and low sensitivity to parameter initialization, in comparison with three classification algorithms, namely LVQ1, MLP, and SVM. Indeed, the proposed method reaches a zero classification error rate for all training datasets, outperforming the best rival  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **LVQ1** & **MLP** & **SVM** & **MCLNN** \\ \hline
**Dataset** & \(\eta\) & \(\eta\) & \(C\) & **[\(m\), \(\zeta\), \(\delta\)]** \\ \hline Breast Cancer (1) & 0.00\% & 89.80\% & 59.81\% & [35.9\%, 100\%, 100\%] \\ Breast Cancer (2) & 97.44\% & 0.00\% & 97.67\% & [100\%, 100\%, 100\%] \\ diabetes & 1.03\% & 0.00\% & 98.62\% & [100\%, 100\%, 100\%] \\ german & 9.23\% & 2.04\% & 98.02\% & [100\%, 100\%, 100\%] \\ ionosphere & 1.03\% & 0.00\% & 3.05\% & [100\%, 100\%] \\ iris & 1.03\% & 68.37\% & 97.32\% & [100\%, 100\%, 100\%] \\ switzerlan & 0.00\% & 0.00\% & 97.77\% & [100\%, 100\%, 100\%] \\ unbalanced & 99.49\% & 100\% & 93.77\% & [100\%, 100\%, 100\%] \\ va & 99.49\% & 0.00\% & 98.22\% & [100\%, 100\%, 100\%] \\ vote & 26.67\% & 1.02\% & 99.17\% & [100\%, 100\%, 100\%] \\ wdbc & 92.31\% & 10.20\% & 98.72\% & [100\%, 100\%, 100\%] \\ wine & 4.10\% & 46.94\% & 98.77\% & [100\%, 100\%, 100\%] \\ wpbc & 94.36\% & 0.00\% & 98.42\% & [100\%, 100\%, 100\%] \\ \hline \hline \end{tabular}
\end{table}
Table 9: Results of the study of the ease of finding satisfactory parameters for the MCLNN training algorithm.  
algorithm, SVM, that reaches the same error rate only for 10 among the 13 used datasets. For testing datasets, the best classification error rate is obtained for 10 testing datasets, which is comparable to the SVM results, despite the fact that the proposed model produces a set of class prototypes that can be used for other tasks than classification, such as data compression, whilst SVM tries to find the best separating boundaries among the \(c\) classes in the data space. These encouraging results prompted the application of the proposed model to real-world problems, and one of our planned projects for the near future concerns its application to real data collected from a \"Partnership for Research and Innovation in the Mediterranean Area\" (PRIMA) project entitled \"Intelligent Irrigation System for Low-cost Autonomous Water Control in Small-scale Agriculture\".  
Conceptualization, M.M. and A.B.; methodology, M.M. and A.B.; software, M.M. and J.E.; validation, M.M. and J.E.; formal analysis, M.M. and J.E.; investigation, M.M.; resources, K.B.; data curation, M.M.; writing--original draft preparation, M.M.; writing--review and editing, M.M., J.E. and K.B.; visualization, M.M.; supervision, A.B.; project administration, M.M. All authors have read and agreed to the published version of the manuscript.  
This work is funded by INTEL-IRRIS-PRIMA SECTION II, 2020 (Projet ID 1560).  
Not applicable.  
Details about the used data and where they can be found are given in Table 2.  
The authors would like to warmly thank the reviewers for their relevant comments that helped in clarifying and improving the content of the paper.  
The authors declare no conflict of interest.