## 6 Results and Discussion
### Decision-Tree Model Results  
To verify the superiority of the proposed framework, we adopt a decision-tree model for a comparison study. The decision-tree model is a predictive model with an attribute structure that represents a mapping relationship between object attributes and object values [51]. A decision tree contains a root node and a number of internal nodes and leaf nodes. The root node contains the entire set of samples, the internal node represents a feature or attribute, and the leaf node represents a class. The path from the root node to each leaf node corresponds to a sequence of decision tests. Decision-tree learning essentially generalizes a set of classification rules from a training set to obtain a decision tree that is less contradictory to the dataset, while having good generalization ability [64]. In this paper, the ID3 algorithm and information gain principle are adopted to calculate and select the attribute with the greatest information gain as the classification attribute. In general, a number of attempts are needed to determine the sample number contained in leaf nodes to achieve good performance. Even a small sample number contained in leaf nodes will considerably affect the generalization ability of the decision-tree model. In this study, the cross-validation method is used to determine the sample number contained in leaf nodes to optimize the decision-tree model. Figure 10 shows the relationship between the number of samples contained in leaf nodes and the cross-validation error. The smaller the cross-validation error, the better is the generalization ability of the decision-tree model. As shown by the red circle in the figure, when the sample number contained in leaf nodes is 28, the cross-validation error is the smallest at 0.0523 and the decision-tree model generalization ability is the best. The decision-tree model is optimized for improved performance using the cross-validation method. During decision-tree model learning, the node partitioning process will be repeated for classifying the training samples as accurately as possible. This sometimes results in overfitting because of too many branches in the decision-tree model. Therefore, the risk of overfitting can be reduced by actively removing some branches. In this study, the original decision-tree is pruned for comparison. The classification accuracy and cross-validation error of the original, optimized and pruned decision-trees are compared to determine the best performance of decision-tree model. Table 4 shows the classification accuracy and cross-validation error of the original, optimized, and pruned decision-tree. Figure 10: Relationship between the number of samples contained in leaf nodes and the cross-validation error. As shown in Table 4, the cross-validation error reduced from 6.76% to 5.23% and the average classification accuracy increased from 91.49% to 93.06% by optimizing the original decision-tree; the cross-validation error reduced from 6.76% to 6.26% and the average classification accuracy increased from 91.49% to 92.51% by pruning the original decision-tree. Therefore, both optimizing and pruning of the original decision-tree can improve the generalization performance of the decision tree, with increased Class 1, Class 2, and average classification accuracy. In particular, the cross-validation error of the optimized decision-tree is smaller than that of the pruned decision-tree, indicating that optimization makes the decision-tree model generalize better than pruning. Therefore, the optimized decision-tree classification accuracy is higher than that of pruning, which is consistent with the above results. In summary, for the recognition of track slab deformation in this study, the original decision-tree needs to be optimized without pruning, and the classification accuracies of Class 1 and Class 2 are 94.96% and 91.15%, respectively, with an average classification accuracy of 93.06%.