## 4 Experiments and Results
### Experimental Results
#### 4.3.4 Ablation Experiment
(2) Ablation experiment based on the classifier ensemble scale  
After obtaining the feature reconstruction space for each high-dimensional dataset, we explored the effect of the number of classifiers (2, 4, 6) on the HDELC ensemble algorithm. The initial set of classifiers was decision tree (DT), random dorest (RT), Adaboost, gradient  
Figure 5: Visualization results of changes in feature space after reconstruction. (**a**) AsianReligionsData, Ad and REJAFADA dataset. (**b**) Arrhythmia, CNAE and Gait dataset. (**c**) Parkinson QSAR and QSAR_oral dataset. (**d**) SCADI, Secom and Urban dataset. boosted (GB), extreme tree (ET), and support vector machine (SVM). Table 4 shows the corresponding parameter settings for the 12 UCI high-dimensional datasets under the HDELC algorithm, including the optimal attribute screening factor \(\beta\) corresponding to different high-dimensional datasets and the order of selection of base classifiers for the model ensemble. For most datasets, the larger the \(\beta\) setting, the higher the classification accuracy tends to be. The possible reason is that high-dimensional-data classification problems often contain more redundant information. Moreover, optimizing some weak discriminative features will enable the classifier to better capture the relationship between input and output variables and help the model improve its generalization performance. It is further demonstrated that the attribute screening factors \(\beta\) can somewhat improve the model's robustness. After the best attribute screening factors' selection, the accuracy of the gradient boosting tree and limit tree is relatively high. The possible reason is that random forest and gradient boosting belong to ensemble algorithms. Both are based on decision trees, which can better handle non-linear relationships, such as the classification of high-dimensional data, and have a powerful fitting ability. In general, their performance is better than that of traditional single classifiers. Moreover, from the perspective of deviation-variance analysis, random forest mainly reduces the variance term of error, while gradient boosting can reduce both variance and deviation. Table 5 shows the accuracy, recall, F1-score and precision performance for the number of ensemble classifiers. Regarding classification accuracy, most datasets have the best performance in each category when the number of integrated classifiers is four, and better performance is achieved on high-dimensional datasets. Meanwhile, the Recall, Precision, and F1-score performances of HDELC-4 on Arrhythmia, Gait, SCADI, Secom, and other datasets are also significantly improved. In summary, the effectiveness and robustness of the HDELC algorithm proposed in this paper are verified. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **The Best of \(\beta\)** & **The Order of Selection for Base Classifiers** \\ \hline AsianReligionsData & 0.5 & \(ET>RF>GB>SVM>DT>Adabosot\) \\ Arrhythmia & 0.4 & \(RF>ET>GB>SVM>Adabosot>DT\) \\ CNAE & 0.1 & \(GB\geq ET>SVM>RF>DT>Adabosot\) \\ Gait & 0.3 & \(ET>RF>GB>SVM>Adabosot>DT\) \\ Ad & 0.4 & \(RF>ET>Adabosot>DT>SVM>GB\) \\ Parkinson & 0.4 & \(GB>ET>Adabosot>RF>DT>SVM\) \\ QSAR & 0.4 & \(GB>ET>Adabosot>RF>DT>SVM\) \\ QSAR\_oral & 0.1 & \(GB\geq Adabosot>SVM>ET>RF>DT\) \\ REJAFADA & 0.4 & \(RF>ET>GB>DT>Adabosot>SVM\) \\ SCADI & 0.2 & \(SVM\geq ET>RF>GB>DT>Adabosot\) \\ Secom & 0.3 & \(ET>GB>RF>SVM>Adabosot>DT\) \\ Urban & 0.5 & \(ET>GB\geq Adabosot>RF>DT>SVM\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Optimal attribute screening factors and classifier ranking order. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Model** & **Accuracy** & **Recall** & **Precision** & **F1-Score** \\ \hline \multirow{3}{*}{AsianReligionsData} & HDELC-2 & 0.8544 & 0.8187 & 0.8299 & 0.8243 \\  & HDELC-4 & **0.8593** & 0.8359 & 0.8168 & **0.8262** \\  & HDELC-6 & 0.8442 & 0.8366 & 0.8078 & 0.8219 \\ \hline \multirow{3}{*}{Arrhythmia} & HDELC-2 & 0.72