# Neighborhood Granule Classifiers
## 5 Experimental Analyses
As can be seen from Figure 4, for the wine dataset, there were 11 maximum values of classification accuracy which were equal to 0.9773, mainly distributed on the plane between the neighborhood parameters ranging from 0.70-0.90 and the similarity thresholds ranging from 0.6-0.85. The minimum values of classification accuracy were mainly distributed in corners with neighborhood parameters being smaller and similarity thresholds being smaller at the same time. From the above experiments, we can see that, when the dataset had a large number of categories, such as glass and segmentation, the classification accuracy was mainly related to the granular similarity threshold. The similarity threshold was larger, and the classification effect was better. When the dataset had a large number of features, such as segmentation and wine, the classification accuracy was mainly related to the neighborhood granulation parameter, and the smaller the neighborhood granulation parameter, the worse the classification effect. In order to compare our algorithms with other classifiers, we ran some experiments on only four datasets. The classification algorithm uses traditional K-Nearest Neighbor (KNN) classifier, and our proposed Neighborhood Granule Classifier was based on Relative (NGCR) distance and the Neighborhood Granule Classifier on Absolute (NGCA) distance. In Table 3, the compared results are shown with a fivefold cross-validation testing for the wine dataset. The test average results of the four UCI datasets are illustrated in Figures 5-8. As can be seen from Figure 5, for the glass dataset, the classification accuracy of KNN was 0.6415, the maximum classification accuracy of NGCR 0.6981, and the maximum classification accuracy of NGCA 0.6038. For the NGCR classifier, the classification accuracy was higher than KNN when the value of neighborhood parameter was 0.9. The NGCR was better than NGCA in any neighborhood parameter value. \begin{table}
\begin{tabular}{c c c c} \hline
**Testing** & **KNN** & **NGCR** & **NGCA** \\ \hline _Accuracy_-1 & 0.9545 & 0.9545 & 1 \\ _Accuracy_-2 & 0.9636 & 1 & 0.9545 \\ _Accuracy_-3 & 0.9773 & 0.9773 & 1 \\ _Accuracy_-4 & 0.9477 & 0.9545 & 0.9545 \\ _Accuracy_-5 & 0.9294 & 1 & 0.9773 \\ _Average_ & 0.9545 & 0.9773 & 0.9773 \\ \hline \end{tabular}
\end{table} 
Table 3: Classification experiment results for Wine data set. Figure 5: Classification accuracy for the glass dataset. As can be seen from Figure 6, for the pima dataset, the classification accuracy of KNN was 0.7448. The classification accuracy of NGCR reached a maximum value of 0.7656, when the value of the neighborhood parameter was 0.6; and the classification accuracy of NGCA reached a maximum of 0.75, when the value of the neighborhood parameter was 0.55. Therefore, the NGCR and NGCA granule classifiers were better than the traditional KNN classifier under a suitable neighborhood parameter value. For the segmentation dataset in Figure 7, the classification accuracy of KNN was 0.7857; while the value of the neighborhood parameter was 0.55; the classification accuracy of NGCR reached a maximum value of 0.8095; and that result was the same as NGCA. It can be seen that NGCR and NGCA were better than the traditional KNN under a suitable neighborhood parameter value. The performance of NGCR was better than that of NGCA. Figure 6: Classification accuracy for the pima dataset. Figure 7: Classification accuracy for the segmentation dataset. As for the wine dataset in Figure 8, the classification accuracy of KNN was 0.9545; the classification accuracy of NGCR reached the maximum value of 0.9773, while the neighborhood parameter values were 0.75, 0.90 and 0.95; and while the neighborhood parameter values were 0.75-0.9, the classification accuracy of NGCA reached the maximum value of 0.9773. It can be seen that NGCR and NGCA were better than the traditional KNN under suitable neighborhood parameter values.