# Recognition Algorithm Based on Improved FCM and Rough Sets for Meibomian Gland Morphology
## 4 Analysis of Experimental Results  
To verify the effectiveness of the method, 40 samples were tested with our proposed method, including eight cases of the normal meibomian gland type, 14 cases of the shortened type, 10 cases of the deletion type, and eight cases of the serious deletion type. These are represented in Figure 9, in the classification labels from 1 to 4, using \"o\". Experimental results showed that 39 samples were classified into the correct category using the improved FCM and rough sets, while 32 cases were classified correctly using the method based on the traditional FCM and rough sets. The quantitative comparison of classification results is shown in Table 5, and comparison results are shown in Figure 9. \begin{table}
\begin{tabular}{c} \hline
**Diagnostic Rule** \\ \hline crs(1) AND con(2) AND reg(3) AND lin(2) \(\Rightarrow\) dec(1) \\ crs(3) AND con(4) AND reg(1) AND lin(3) \(\Rightarrow\) dec(2) \\ crs(1) AND con(2) AND reg(4) AND lin(1) \(\Rightarrow\) dec(3) \\ crs(4) AND con(2) AND reg(1) AND lin(1) \(\Rightarrow\) dec(4) \\ crs(1) AND con(1) AND reg(4) AND lin(1) \(\Rightarrow\) dec(2) \\ crs(4) AND con(5) AND reg(2) AND lin(4) \(\Rightarrow\) dec(1) \\ crs(2) AND con(4) AND reg(3) AND lin(4) \(\Rightarrow\) dec(2) \\ \hline \(\ldots\) \\ \hline crs(2) AND con(2) AND reg(3) AND lin(4) \(\Rightarrow\) dec(1) \\ crs(4) AND con(3) AND reg(2) AND lin(1) \(\Rightarrow\) dec(3) \\ crs(2) AND con(1) AND reg(4) AND lin(1) \(\Rightarrow\) dec(4) \\ crs(3) AND con(3) AND reg(3) AND lin(4) \(\Rightarrow\) dec(2) \\ crs(4) AND con(5) AND reg(1) AND lin(4) \(\Rightarrow\) dec(4) \\ crs(1) AND con(2) AND reg(4) AND lin(3) \(\Rightarrow\) dec(4) \\ crs(3) AND con(1) AND reg(1) AND lin(3) \(\Rightarrow\) dec(3) \\ crs(2) AND con(2) AND reg(3) AND lin(3) \(\Rightarrow\) dec(1) \\ crs(3) AND con(4) AND reg(1) AND lin(4) \(\Rightarrow\) dec(3) \\ \hline \end{tabular}
\end{table} 
Table 4: Typical diagnostic rule generated using the proposed method. Figure 8: The flow chart of attribute reduction using rough sets algorithm. From the Figure 9, it can be seen that 32 cases in the 40 samples were classified to the correct classification using the traditional FCM and rough sets, and the recognition rate was only 80% because the clustering process was affected by isolated points and the clustering centers were randomly selected. The improved FCM algorithm overcame the defects of the traditional FCM, which is sensitive to initial clustering and is susceptible to isolated points, and produced higher quality clustering results. Attribute reduction preserved more accurate classification information, and thus formed clearer and simpler classification rules. Our proposed method successfully classified 39 of 40 samples, with a recognition rate as high as 97.5%. In order to further evaluate the proposed algorithm objectively, we used the \(n\)-fold cross validation to verify the accuracy of the algorithm. Through a large number of experiments, here we set \(n=4\), that is, the 136 data samples were divided into four copies \(n_{1}\), \(n_{2}\), \(n_{3}\), and \(n_{4}\) and each copy has 34 samples. Three of them were used as training samples and one was used as the test sample and made four simulation experiments alternately. The average accuracy of the four results with a recognition rate as high as 98.5%, was used as an estimate of the accuracy of the algorithm. The classification results are shown in Table 6. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Simulation** & **Test Samples** & **Training** & **The Number of Correct** & **Accuracy** & **The Average** \\
**Time** & **(34)** & **Samples (t02)** & **Classified Samples** & & **Accuracy** \\ \hline
1 & \(n_{1}\) & \(n_{2}\), \(n_{3}\), \(n_{4}\) & 33 & 97\% & \\
2 & \(n_{2}\) & \(n_{1}\), \(n_{3}\), \(n_{4}\) & 33 & 97\% & \\
3 & \(n_{3}\) & \(n_{1}\), \(n_{2}\), \(n_{4}\) & 34 & 100\% & \\
4 & \(n_{4}\) & \(n_{1}\), \(n_{2}\), \(n_{3}\) & 34 & 100\% & \\ \hline \hline \end{tabular}
\end{table}