## 4 Experiments and Results
### Experimental Results
#### 4.3.6 Significance Test  
We further used several non-parametric tests to determine the significance of the performance differences between the HDELC algorithm and the different ensemble algorithms, and the adjusted \(p\)-values associated with the various non-parametric tests are presented in Tables 8 and 9. Based on the adjusted \(p\)-values associated with the different nonparametric tests listed in Tables 8 and 9, comparing each adjusted p-value with the significance threshold, most \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset** & **HDELC** & **Stacking** & **Bagging** & **Boosting** & **BOA [34]** & **JMO-FSCD [35]** & **PCA-Decision Tree [36]** \\ \hline AsianReligionsData & **0.8651** & 0.8361 & 0.8312 & 0.8367 & 0.8518 & 0.8633 & 0.8489 \\ Arrhythmia & 0.7397 & 0.7225 & **0.7411** & 0.7235 & 0.7361 & 0.7402 & 0.7296 \\ CNAE & **0.9444** & 0.9152 & 0.8600 & 0.9189 & 0.9102 & 0.9428 & 0.9225 \\ Gait & **0.7000** & 0.7000 & 0.6000 & 0.6800 & 0.7000 & 0.6808 \\ Ad & **0.9842** & 0.9712 & 0.9534 & 0.9430 & 0.9847 & **0.9853** & 0.9823 \\ Parkinson & **0.8940** & 0.8921 & 0.8917 & 0.8932 & **0.8940** & **0.8944** & 0.8924 \\ QSAR & **0.9142** & 0.9108 & 0.9000 & 0.9000 & 0.9126 & 0.9132 & 0.9044 \\ QSAR, oral & **0.9989** & 0.9984 & 0.9802 & 0.9885 & 0.9892 & 0.9986 & **0.9989** \\ REJAFDA & 0.9844 & 0.9825 & 0.9813 & **0.9862** & 0.9837 & 0.9836 & 0.9840 \\ SCADI & 0.7857 & **0.7910** & 0.8427 & 0.7648 & 0.7829 & 0.7806 & 0.7844 \\ Secom & **0.9586** & 0.9562 & 0.9278 & 0.9531 & 0.9554 & 0.9546 & 0.9569 \\ Urban & **0.9444** & 0.9430 & 0.9301 & 0.9429 & 0.9355 & 0.9438 & 0.9381 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Accuracy of advanced aggregation techniques for high-dimensional data, where the bold values denote that the best value of the indicator under the dataset. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithm** & **Computational Complexity** \\ \hline BEWOA [37] & \(O(L\cdot N\cdot n)\) \\ LightTS [38] & \(O(E\cdot B\cdot P\cdot w)\) \\ AEEF [39] & \(O(D\cdot d\cdot m\cdot K\cdot n^{2})\) \\ HDELC & \(O(l\cdot n\cdot\log m)\) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of computational complexity of existing algorithms. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithm** & **Unadjusted \(p\)** & \(P_{[PERSON]}\) & \(P_{[PERSON]}\) & \(P_{[PERSON]}\) & \(P_{[PERSON]}\) \\ \hline Stacking & 0.0037 & 0.0158 & 0.0064 & 0.0064 & 0.0064 \\ Bagging & 0.0012 & 0.0069 & 0.0038 & 0.0038 & 0.0038 \\ Boosting & 0.0056 & 0.0298 & 0.0085 & 0.0085 & 0.0085 \\ BOA [34] & 2.3006 \(\times\)\(10^{-5}\) & 2.0887 \(\times\)\(10^{-4}\) & 5.9823 \(\times\)\(10^{-5}\) & 5.9823 \(\times\)\(10^{-5}\) & 5.9823 \(\times\)\(10^{-5}\) \\ JMO-FSCD [35] & \(5.3924\times\)\(10^{-10}\) & 2.4306 \(\times\)\(10^{-9}\) & 2.6731 \(\times\)\(10^{-9}\) & 2.6731 \(\times\)\(10^{-9}\) \\ PCA-Decision Tree [36] & \(1.4489\times\)\(10^{-6}\) & 1.0351 \(\times\)\(10^{-5}\) & 1.0517 \(\times\)\(10^{-6}\) & 1.0517 \(\times\)\(10^{-6}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Adjusted \(p\)-values for nonparametric tests (Bonferroni-Dunn, Holm, Hochberg, and Hommel tests; HDELC is the control; bold results indicate \(p>\) 0.0500).