## 4 Experiments and Results
### Experimental Results
#### 4.3.5 Comparison of Advanced Aggregation Techniques for High-Dimensional Data  
This chapter also compares HDELC with three primary traditional integrated classifiers and three integration techniques for high-dimensional data that have performed well in the existing research. Table 6 shows the performance of different integrated classifiers for high-dimensional data. It shows that the HDELC algorithm performs well on most datasets with an accuracy improvement of about 0.14% to 3.08% compared to the traditional integrated classifiers. Compared to the three integration techniques for high-dimensional data that have performed well in existing studies, the HDELC algorithm achieves better results, with an accuracy improvement of 2.2% compared to the best solution reported in the literature [34] and a certain degree of accuracy improvement compared to the JMO-FSCD algorithm proposed in the literature [35].  
Meanwhile, compared with the current state-of-the-art integrated classification models, the HDELC algorithm proposed in this study has a lower computational complexity, which means that the HDELC algorithm is relatively efficient and performant and is more effective and reliable in dealing with large-scale data. Table 7 shows the comparison of the computational complexity of existing algorithms.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Dataset** & **Model** & **Accuracy** & **Recall** & **Precision** & **F1-Score** \\ \hline \multirow{4}{*}{CNAE} & HDELC-2 & 0.8888 & 0.9098 & 0.8915 & 0.9006 \\  & HDELC-4 & 0.9231 & 0.9110 & **0.9089** & **0.9099** \\  & HDELC-6 & **0.9437** & **0.9119** & 0.9080 & **0.9099** \\ \hline \multirow{4}{*}{Gait} & HDELC-2 & 0.7000 & 0.6838 & 0.6822 & 0.6830 \\  & HDELC-4 & **0.7000** & **0.7676** & **0.7512** & **0.7593** \\  & HDELC-6 & 0.6000 & 0.6172 & 0.5975 & 0.6072 \\  & HDELC-2 & 0.9548 & 0.9602 & 0.9630 & 0.9616 \\  & HDELC-4 & **0.9845** & **0.9653** & 0.9579 & **0.9616** \\  & HDELC-6 & 0.9802 & 0.9728 & 0.9732 & 0.9730 \\ \hline \multirow{4}{*}{Parkinson} & HDELC-2 & 0.8945 & 0.8882 & 0.8862 & 0.8872 \\  & HDELC-4 & 0.8941 & 0.8808 & **0.8863** & 0.8835 \\  & HDELC-6 & 0.8872 & 0.8721 & 0.8647 & 0.8684 \\  & HDELC-2 & 0.9004 & 0.8969 & 0.8870 & 0.8919 \\  & HDELC-4 & **0.9152** & **0.9012** & 0.9217 & 0.9113 \\  & HDELC-6 & 0.9041 & 0.8991 & 0.9271 & 0.9129 \\ \hline \multirow{4}{*}{QSAR\_oral} & HDELC-2 & 0.9921 & 0.9908 & 0.9910 & 0.9909 \\  & HDELC-4 & **0.9986** & **0.9961** & **0.9950** & **0.9955** \\  & HDELC-6 & 0.9959 & 0.9934 & 0.9949 & 0.9941 \\ \hline \multirow{4}{*}{REJAFADA} & HDELC-2 & **0.9844** & **0.9709** & **0.9724** & **0.9716** \\  & HDELC-4 & 0.9826 & 0.9697 & 0.9653 & 0.9675 \\  & HDELC-6 & 0.9733 & 0.9548 & 0.9429 & 0.9488 \\ \hline \multirow{4}{*}{SCADI} & HDELC-2 & 0.7842 & 0.7697 & 0.7539 & 0.7617 \\  & HDELC-4 & **0.7861** & **0.8009** & **0.8045** & **0.8027** \\  & HDELC-6 & 0.7622 & 0.7535 & 0.7463 & 0.7499 \\ \hline \multirow{4}{*}{Secom} & HDELC-2 & 0.9488 & 0.9409 & 0.9431 & 0.9420 \\  & HDELC-4 & **0.9593** & **0.9443** & **0.9457** & **0.9450** \\ \cline{1-1}  & HDELC-6 & 0.9531 & 0.9441 & 0.9355 & 0.9398 \\ \hline \multirow{2}{*}{Urban} & HDELC-2 & **0.9452** & 0.9432 & 0.9305 & 0.9368 \\ \cline{1-1}  & HDELC-4 & 0.9355 & 0.9490 & 0.9314 & 0.9401 \\ \hline \end{tabular}
\end{table}
Table 5: _Cont._