# A Classification Model Based on Interval Rule Inference Network with Interpretability
## 6 Experiment and Analysis
### Application in Mechanical Properties of Coarse Aggregate of Reinforced Recyclled Clay Brick  
The utilization of recycled brick aggregate as a coarse aggregate in concrete enhances the recycling of waste bricks and reduces resource depletion mixtures. It is meaningful in environmental protection and economy, predicting the classification of the mechanical properties of coarse aggregate of reinforced recycled clay brick in concrete. However, the process requires interpretability and the results should be intervenable, while the history data for training are small. In this study, the proposed IRIN for classification is applied. The data containing 72 samples with five attributions--apparent density \(\rho\), water absorption rate (\(\omega\)), crushing value (\(\sigma\)), substitution rate (\(\lambda_{g}\)), and glue-water ratio were collected from Zhongtu International Architectural Design Co. Ltd. (Shijiazhuang, China) and Hebei University of Science and Technology (Shijiazhuang, China) [33]. They are divided into two categories, good mechanical property and bad mechanical property, depending on the compressive strength. In this experiment, a five-fold cross-validation method was employed to evaluate the performance of the proposed method in identifying the mechanical properties. The evaluation metrics for classifying performance include the following:  
1. Precision: The proportion of true positive predictions out of all positive predictions. 2. Recall: The proportion of true positive predictions out of all actual positives. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Iris** & **Seeds** & **Ecoli** & **Glass** & **Diabets** & **Average Rank** \\ \hline EBRB [10] & 95.2\%(9) & 87.1\%(9) & 81.2\%(8) & 51.4\%(10) & - & 36(11) \\ Yang- & 95.8\%(6) & 91.2\%(7) & 59.3\%(13) & 58.8\%(9) & - & 35(9) \\ EBRB [29] & 98.6\%(1) & 94.3\%(3) & 78.3\%(9) & 69.1\%(7) & 72.9\%(3) & 23(5) \\ FG-BRB [18] & 95.3\%(8) & 91.2\%(7) & 75.3\%(11) & 68.2\%(8) & 72.7\%(4) & 38(12) \\ RD-BRB [18] & 98.3\%(2) & 94.2\%(4) & 75.8\%(10) & 68.2\%(8) & 72.7\%(4) & 28(8) \\ DT-BRB [31] & 96.0\%(5) & 94.3\%(3) & 82.5\%(6) & 72.9\%(4) & - & 18(3) \\ AP-EBRB [21] & 95.8\%(6) & 97.6\%(1) & 87.1\%(2) & 50.0\%(11) & - & 20(4) \\ DEA- & 95.4\%(7) & 91.7\%(6) & 83.3\%(5) & 69.5\%(6) & 74.2\%(2) & 26(7) \\ EBRB [32] & 97.3\%(3) & 93.3\%(5) & 84.6\%(4) & 70.4\%(5) & - & 17(2) \\ MTS-BRB [19] & 96.0(5) & 87.1\%(9) & 81.5\%(7) & 73.4\%(2) & 68.0\%(5) & 28(8) \\ FBDT [20] & 96.0\%(5) & 85.2\%(10) & 84.6\%(4) & 65.4\%(12) & 75.3\%(1) & 31(9) \\ RIN [17] & 96.6(4) & 90.5\%(8) & 84.8\%(3) & 60.5\%(7) & - & 22(5) \\ IRIN & 96.6\%(4) & 95.2\%(2) & 88.1\%(1) & 76.7\%(1) & 75.3\%(1) & 9(1) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of classification results with other BRB systems. F1 score: The harmonic mean of precision and recall, considering both accuracy and recall metrics. The calculation formula is as follows:  
\[F_{1}=2\times\frac{precision\times recall}{precision+recall} \tag{68}\]  
The comparison of the performance metrics for the seven classification methods is shown in Figure 4. As can be seen from Figure 4, the multilayer perceptron classifier (MLP) and support vector classifier (SVC) have poor performance, while the remaining six methods' classification performance from best to worst are the proposed IRIN, random forest classifier (RFC), decision tree classifier (DTC), RIN, XGBoost (XGB), and K-nearest neighbors classifier (KNC). The proposed IRIN classification method ranks in the top two in all three performance indicators. This shows that IRIN automatically generates reasonable IBRB, which effectively improves the inference effect of IBRB, and then improves the performance of IRIN.