## 4 Experimental Results and Discussion
### Maceral Composition Classification  
Each microscopic image was divided into a series of discrete objects (i.e., macroals groups). Then we extracted geometric, grayscale, texture features from each object, and created a 172-dimensional feature vector for each object. We compared the recognition performance of random forest with five popular classification methods via a 10-fold cross validation. The experiments were repeated 10 times and the average accuracies were reported in Table 4. This summarizes the classification results of all the 973 objects obtained by image segmentation. The proposed approach yields a high accuracy of 90.44%, outperforming other classifiers. This should be regarded as a very good performance, especially when we take into account the high degree of complexity of coals. The obtained results show that the proposed strategy based on image segmentation and classification has a high potential for macroal components identification. We further test the performance of an individual kind of features. The recognition performance using texture features is much higher than that of the other two types of features. Generally, the fusion of multiple kinds of features can achieve a significant improvement over a single kind of features, except for weighted KNN classifier. To observe relations between the predictions of classifiers and the true labels, we also employ confusion matrices to report the results of different approaches. The confusion matrix enables us to know not only the error rates being made by a classifier but also the types of errors. More specifically, the rows of the matrix represent the predicted class, and the columns correspond to the true class (i.e., ground truth). The green diagonal cells stand for the number of correctly classified observations, while the red \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Classification Accuracy (\%) \(\pm\) std (\%)**} \\ \hline
**Classifier** & **Geometric Features** & **Grayscale Features** & **Texture Features** & **All Features** \\ \hline Fine Tree & \(72.29\pm 0.72\) & \(70.01\pm 0.83\) & \(78.79\pm 1.02\) & \(85.71\pm 1.08\) \\ Radial basis function kernel & \(72.47\pm 0.11\) & \(76.97\pm 0.59\) & \(85.54\pm 0.33\) & \(86.62\pm 0.58\) \\ support-vector machine & \(55.22\pm 0.49\) & \(71.40\pm 0.56\) & \(80.44\pm 0.47\) & \(78.69\pm 0.88\) \\ Weighted K-Nearest Neighbors & \(53.76\pm 0.32\) & \(70.34\pm 0.46\) & \(82.12\pm 0.40\) & \(83.36\pm 0.60\) \\ Subspace K-Nearest Neighbors & \(55.45\pm 0.74\) & \(71.13\pm 0.68\) & \(79.61\pm 0.56\) & \(81.26\pm 0.47\) \\ Random Forest & \(79.30\pm 0.47\) & \(78.86\pm 0.34\) & \(85.64\pm 0.30\) & \(90.44\pm 0.37\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparing the results of random forest and the other 5 machine learning methods for identifying macroal components (10-fold cross-validation, and repeat the experiment 10 times). Figure 5: The results of image segmentation based on (**a**) the proposed Maceral Identification strategy based on image Segmentation and Classification (MISC) and (**b**) the fixed-size window strategy. (a-0) and (b-0) denote the original image. (a-1) and (b-1) are the enlargement of spornites, (a-2) and (b-2) are the enlargement of micrinites, (a-3) and (b-3) are the enlargement of fusinites. cells represent the misclassified observations. The precision and the recall rate corresponding to each class are also shown at the far right of each row and the bottom of each column, respectively. The overall accuracy is shown at the bottom-right corner of the matrix. As shown in Figure 6, the proposed method provides satisfying performance for most of the maceral components. The main flaws of all these six classifiers come from the misclassification of semifusinite and inertodetrinite.