# Application of Data Mining Techniques to Predict Luminance of Pavement Aggregate
## 4 Analysis of the Research Results
### Evaluation of the Effectiveness of Selected DM Techniques
To counteract this, the BT model also took into account the occurrence of the same aggregate, i.e., limestone and quartzite from two different mines in the Swietokrzyskie voivodeship, Poland. The rules for creating a boosted tree are easy to interpret, but the notation of the established scoring system assigned to the nodes is very complex. Therefore, the end result of using BT is a model encoded in the form of an XML (Extensible Markup Language) file, which was created to represent various data in a structured way that allows for easy implementation into other data analysis systems. It is currently the best language for data presentation, recommended by the W3C organization [56]. From the perspective of practical applications, the set of rules in graphical form is the most useful (Figure 7). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**DM Method** & **MAE** & **RMSE** & **R\({}^{2}\)** \\ \hline RF & 4.5 & 42.7 & 0.95 \\
**BT** & **4.3** & **37.9** & **0.96** \\ ANN & 4.7 & 44.6 & 0.95 \\ C \& RT & 4.9 & 48.9 & 0.95 \\ SVM & 14.5 & 268.2 & 0.80 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Summary of regression metrics of fit quality. Figure 7: Structure (rules) of the results using the BT technique. It should be noted that the average result of the luminance coefficient Qd of the entire set (ID = 1) is 73.9 mcd m\({}^{-2}\) lx\({}^{-1}\), whereas the scatter of the results (variance) is significant at 499 mcd m\({}^{-2}\) lx\({}^{-1}\). Thus, that a set of rules can be built to quickly distinguish Qd values against different aggregates. A distinct group can be observed first, which are aggregates of limestone and quartzite, which have the greatest ability to brighten the potential pavement. Rules were defined for this \"leaf\" in the BT model, which did not further split (Mean(Qd) = 90.1 mcd m\({}^{-2}\) lx\({}^{-1}\) for ID = 2). In contrast, much greater variation (larger ruleset) was obtained for the other darker-colored aggregates (Mean(Qd) = 52.1 mcd m\({}^{-2}\) lx\({}^{-1}\) for ID = 2). As a result of the distribution, basalt aggregate achieved an average value of Qd = 40 mcd m\({}^{-2}\) lx\({}^{-1}\), while melaphyre achieved 49 mcd m\({}^{-2}\) lx\({}^{-1}\) with the smallest scatter of results. In the cases of gabbro and amphibolite, the value of the predicted luminance was affected by additional rules related to grain size. Therefore, it can be presumed that the texture or shape of the aggregate affected the value of the luminance coefficient. [PERSON] et al., also came to similar conclusions in their paper [11]. Evaluating the structure of the tree (Figure 7) as a whole is quite complex, particularly when there are more \"leaves\", however, some useful properties that can help with this can be derived. The most commonly used summary is the parameter characterizing the importance of a feature, indicating how important each feature was for decision processes in the regression tree. This is a number between 0 and 1 for each function, where 0 denotes \"not used at all\" and 1 denotes \"perfectly predicts the objective\". The significance of features always adds up to 1. The validity of the features can be visualized, as shown in Figure 8.  
The results in Figure 8 confirm the fact that luminance and related rules were most influenced by aggregate petrography. The second most important factor was nighttime visibility RL, with aggregate grain size being of equal importance. In contrast, test conditions that take into account the preparation of the aggregate surface during the test had a marginal effect on the prediction of the luminance coefficient Qd. Therefore, the mineral composition and treatment of the aggregate and its potential texture had the most significant impact on the aggregate luminance coefficient Qd. A summary of the effect of grain size variation and petrography on the Qd result is shown in Figure 9.  
Figure 8: Validity chart of BT model predictors. The similarity between quartzite, granite, and limestone aggregate is observed in the range of the finest 0/2 fraction.