## 3 Data Preparation and Pre-Processing
### Data Processing  
In the data processing stage, missing values are encoded as 0, and categorical variables such as the main roof type, immediate roof type, and floor type are sequentially encoded. Non-standard and anomalous categories are assigned initial codes starting from 10 to allow for incremental growth. Subsequently, box plots are used to visually analyze and identify outliers in the data. The statistical method is based on six key statistical values used as identification points: the upper and lower quartile lines, upper and lower edges, median line, and mean point. This method enables the intuitive visualization of outliers. When data points exceed the upper or lower edge lines, they are classified as outliers [45]. The dataset with outliers and missing values is cleaned, resulting in a usable dataset of 1273 instances. A random starting point is selected, with 80% of the total data used for training and 20% for testing. The training set contains 1018 instances, and the testing set contains 255 instances. Some of the feature box line plots with outliers are shown in Figure 2.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Feature Source** & **Feature Coding** & **Feature** & **Feature Type** & **Unit** \\ \hline \multirow{4}{*}{Hydraulic support} & d\_1 & Minimum support height of & \multirow{2}{*}{Numerical value} & m \\  & & hydraulic support & & \\ \cline{2-2}  & d\_2 & Maximum support height of & \multirow{2}{*}{Numerical value} & m \\  & & hydraulic support & & \\ \cline{2-2}  & d\_3 & Support strength & Numerical value & Mpa \\ \cline{2-2}  & d\_4 & Working resistance & Numerical value & kN \\ \hline \multirow{4}{*}{Shearer} & d\_5 & Fully mechanized mining face & \multirow{2}{*}{Numerical value} & t \\  & & cycle yield & & \\ \cline{2-2}  & d\_6 & Number of cuts & Numerical value & - \\ \cline{2-2}  & d\_7 & Cutting depth & Numerical value & m \\ \cline{2-2}  & d\_8 & Average hauling speed & Numerical value & m/min \\ \cline{2-2}  & d\_9 & Maximum hauling speed & Numerical value & m/min \\ \cline{2-2}  & d\_10 & Maximum production capacity & Numerical value & t/h \\ \cline{2-2}  & d\_11 & Installed power & Numerical value & kW \\ \hline Scraper conveyor & d\_12 & Conveying capacity & Numerical value & t/h \\ \hline \hline \end{tabular}
\end{table}
Table 2: Output feature variables.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline c\_1 & c\_2 & c\_3 & c\_4 & c\_5 & c\_6 & c\_7 & \(\ldots\) & d\_3 & d\_6 & d\_10 & d\_12 \\ \hline III & Ia & II & 2.8 & 247 & 12.1 & 11.7 & \(\ldots\) & 1.47 & 14 & 5000 & 4500 \\ II & II & II & II & 1 & 461 & 9.3 & 4.58 & \(\ldots\) & 1.1 & 20 & 1600 & 1500 \\ II & II & II & 3.4 & 334 & 15.2 & 6.09 & \(\ldots\) & 1.2 & 12 & 3000 & 3000 \\ \(\ldots\) & \(\ldots\) & \(\ldots\) & \(\ldots\) & \(\ldots\) & \(\ldots\) & \(\ldots\) & \(\ldots\) & \(\ldots\) & \(\ldots\) & \(\ldots\) \\ IVa & Ia & II & 3.3 & 616 & 20.3 & 8.18 & \(\ldots\) & 0.69 & 8 & 1271 & 1000 \\ III & Ib & IIIb & 1.2 & 472 & 18.1 & 3.11 & \(\ldots\) & 1.55 & 18 & 800 & 700 \\ I & III & I & 3.1 & 189 & 14.8 & 3.38 & \(\ldots\) & 0.53 & 8 & 1800 & 1600 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Partial sample data.