# Self-Learning Mechanism for Mobile Game Adjustment towards a Player
## 5 Player Classification Using Neural Networks
### Results and Discussion  
The proposed approach has been tested using NN in three experimental settings: NN trained with 150 feature vectors (NN\({}_{1}\)), NN trained with 300 feature vectors (NN\({}_{2}\)) and NN trained with 450 feature vectors (NN\({}_{3}\)). An evaluation of the player recognition results was performed with three test sets containing 50, 100, 150 feature vectors, respectively. The average recognition accuracies for all experimental settings are shown in Table 3. All the player categories achieved a high average recognition rate (above 94%) in all experimental settings. It can be noted that the recognition rates of all three player classes are the highest in the case of NN\({}_{3}\), ranging from 95.9% (senior) to 96.9% (progressive). Regarding the player categories, the senior category has slightly lower recognition rates compared to the progressive and kid category in all settings. The progressive class is the class with the highest recognition rate due to its better class separability, while the kid and senior classes are closer in the feature space, which results in slightly lower recognition rates for these two categories. From Table 3, it can be noted that the average recognition accuracy for the kid category is 96.2%, for the progressive category is 96.6%, and for the senior category is 95.4%. Regarding all player categories, the average recognition accuracy is 96.1%. To achieve better insight into the classification performance, classification results from all experimental settings are given in terms of precision, recall and F\({}_{1}\) score. The experi \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Trained Classifier**} & \multicolumn{3}{c}{**Player Category**} \\  & **Kid** & **Progressive** & **Senior** \\ \hline NN\({}_{1}\) & 96.2 & 96.1 & 94.5 \\ \hline NN\({}_{2}\) & 95.9 & 96.8 & 95.9 \\ \hline NN\({}_{3}\) & 96.6 & 96.9 & 95.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Average recognition accuracy [%] in all experimental settings. mental results of the first experimental setting with NN\({}_{1}\) tested with three sets (50, 100, 150 feature vectors), are presented in Table 4. Due to a balance of the three classes in the test sets, it can be noted that all three player categories have high precision, recall and F\({}_{1}\) score correlated with the recognition accuracies. Small variations of the results in three test settings are the result of not perfectly equal class distributions in the test sets. The results from the second experimental setting obtained using NN trained with 300 feature vectors (NN\({}_{2}\)) and tested with three sets (50, 100, 150 feature vectors) are shown in Table 5. Comparison of the results from Tables 4 and 5 indicates that recall for the progressive and senior categories slightly increased in the case of NN\({}_{2}\). Regarding precision, there is an increase for kid and progressive, but a small decrease for senior in the case of NN\({}_{2}\). As for the F\({}_{1}\) score, an increase can be noted in all three categories in the case of NN\({}_{2}\), which is a result of the network training with more samples. Finally, the results from the third experimental setting obtained using NN\({}_{3}\) (trained with 450 feature vectors) and tested with three sets (50, 100, 150 feature vectors) are presented in Table 6. Analysis of the classification results given in Tables 5 and 6 shows that recall increased for all categories in the case of NN\({}_{3}\). On the other hand, there was a slight decrease in precision for the progressive and kid categories, but for the senior category, increased precision was achieved. The presented recognition results indicate that the model is well-trained and the average recognition accuracies show very high recognition rates, which are verified through calculated precision, recall and F\({}_{1}\) score in all experimental settings.