## 5 Experiments
### Establishment of Dataset of Shearer Drum Height Prediction Model
#### 5.1.2 Data Preprocessing  
The prediction of shearer drum adjustment is a complex problem based on data mining. The quality of the data determines the quality of the model extracted by the algorithm, and when the sample quality is poor, it is difficult to propose that the model achieves the desired effect. Therefore, in the data preprocessing stage, the original dataset is processed to remove missing values and outliers to build a high-quality dataset [38].  
Some data in the original sample data are missing or abnormal. For samples with missing one-dimensional data, this paper uses Lagrangian interpolation to impute the missing values. For a sampling point with multiple attributes that are true or abnormal, the sample is deleted to ensure accuracy. It is inevitable to encounter some outliers due to  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Status Attribute** & **Feature** & **Feature Name** & **Feature Type** & **Unit** \\
**Object** & **Coding** & & & \\ \hline \multirow{6}{*}{Shearer status attribute} & \(c_{1}\) & Shearer location & Numerical & m \\  & \(c_{2}\) & Pitch angle & Numerical & \({}^{\circ}\)C \\  & \(c_{3}\) & Right traction temperature & Numerical & A \\  & \(c_{4}\) & Right traction current & Numerical & \({}^{\circ}\)C \\  & \(c_{5}\) & Left traction temperature & Numerical & A \\  & \(c_{6}\) & Left traction current & Numerical & A \\  & \(c_{7}\) & Traction direction & Boolean & Left/right \\  & \(c_{8}\) & Traction speed & Numerical & m/min \\ \hline \multirow{6}{*}{Drum status attribute} & \(c_{9}\) & Right drum temperature & Numerical & \({}^{\circ}\)C \\  & \(c_{10}\) & Right drum current & Numerical & A \\  & \(c_{11}\) & Right drum height change & Numerical & m \\  & \(c_{12}\) & Left drum temperature & Numerical & \({}^{\circ}\)C \\  & \(c_{13}\) & Left drum current & Numerical & A \\  & \(c_{14}\) & Left drum height change & Numerical & m \\  & \(c_{15}\) & Roll angle & Numerical & \({}^{\circ}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Initial feature parameters.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Region Roller Decision Attribute Classification** & **Left Roller Decision Attribute Classification** \\ \hline
**Decision Marker** & **Decision Implications** & **Decision Marker** & **Decision Implications** \\ \hline \(d_{r}(0)\) & drum unchanged & \(d_{l}(0)\) & drum unchanged \\ \(d_{r}(1)\) & drum rises & \(d_{l}(1)\) & drum rises \\ \(d_{r}(2)\) & drum lowers & \(d_{l}(2)\) & drum lowers \\ \hline \hline \end{tabular}
\end{table}
Table 2: Feature decision meaning table.  
As can be seen from Tables 5 and 6, for the left drum condition attribute set, deleting the condition attributes \(c_{5},c_{12}\) from the condition attribute set \(B\) does not affect the classification ability of \(B\) for \(D\), so the final attribute reduction result of the left drum condition attribute set is \(P_{l}=\{c_{1},c_{2},c_{4},c_{6},c_{8},c_{13},c_{14},c_{15}\}\); and for the right drum condition attribute set, deleting the condition attributes \(c_{3},c_{6}\) from the condition attribute set \(B\) does not affect the classification ability of \(B\) for \(D\), so the final reduction result of the right drum condition attribute set is \(P_{r}=\{c_{1},c_{2},c_{4},c_{6},c_{8},c_{10},c_{11},c_{15}\}\).