## 4 Experiment Setup
### Analysis of Experimental Results of Different Datasets with Different Feature Selection Methods
Its RMSE value is 6.02% lower than RFHFS, slightly lower than CSFFS, and 14.35% lower than XGBFS and the Baseline. Among the three feature selection algorithms under the Gaussian process regression algorithm, the FSNT algorithm achieves the smallest RMSE value, which is approximately 13.72% lower than the Baseline. In the Superconductivity dataset, the FSNT algorithm achieves an exceptionally low RMSE compared to other methods. The RMSE of FSNT is 0.0110, while CSFFS has an RMSE of 0.1062 and XGBFS has an RMSE of 0.1800, making FSNT only one-tenth of the magnitude of the other methods. Only RFHFS approaches the performance of FSNT, but the RMSE value of the Baseline is 0.0494, making FSNT 77.73% lower than the Baseline. Under the elastic net regression algorithm, the RMSE values of the four feature selection algorithms and the Baseline are the same value. Under the extra trees regression algorithm, FSNT achieves an RMSE value that is 8.16% lower than that of XGBFS, which is comparable to \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Dataset** & \begin{tabular}{c} **Feature** \\ **Selection** \\ **Method** \\ \end{tabular} & **LR** & **ENR** & **RF** & **ET** & **SVM** & **GBR** & **DTR** & **ABR** & **GPR** & **MLLPR** \\ \hline \multirow{4}{*}{\begin{tabular}{c} Student- \\ por \\ \end{tabular} } & CSFFS & 2.8401 & 3.0566 & 3.0000 & 3.0553 & 2.9342 & 2.8461 & 3.9514 & 2.9749 & 3.1705 & 3.1648 \\  & XGBFS & 2.8081 & 3.0566 & 2.9088 & 2.9186 & 2.8815 & 2.7792 & 3.6470 & 2.9224 & 3.1947 & 2.9115 \\  & RFHFS & 2.8374 & 3.0670 & 3.0013 & 3.0578 & 2.9390 & 2.8909 & 4.0737 & 3.1024 & 3.1925 & 2.9329 \\  & FSNT & 3.0641 & **3.0126** & **2.8695** & 3.1101 & **2.8602** & 3.0243 & **3.5585** & 3.1701 & **3.1086** & 3.1230 \\  & Baseline & 2.8081 & 3.0566 & 2.9088 & 2.9186 & 2.8815 & 2.7792 & 3.6470 & 2.9224 & 3.1947 & 2.9115 \\ \hline \multirow{4}{*}{\
\begin{tabular}{c} Online \\ News \\ \end{tabular} } & CSFFS & 0.1038 & 0.1035 & 0.1114 & 0.1045 & 0.5056 & 0.1158 & 0.1391 & 0.1350 & 0.1421 & 2.0169 \\  & XGBFS & 0.1029 & 0.1035 & 0.1116 & 0.1048 & 0.5056 & 0.1154 & 0.1335 & 0.1541 & 0.1595 & 1.7150 \\  & RFHFS & 0.1035 & 0.1035 & 0.1115 & 0.1047 & 0.5056 & 0.1185 & 0.1338 & 0.1411 & 0.1633 & 1.8974 \\  & FSNT & 0.1041 & **0.1035** & **0.1112** & 0.1050 & **0.5056** & 0.1186 & 0.1462 & **0.1317** & **0.1409** & 2.0082 \\  & Baseline & 0.1035 & 0.1035 & 0.1120 & 0.1046 & 0.5056 & 0.0179 & 0.1313 & 0.1561 & 0.1158 & 2.1284 \\ \hline \multirow{4}{*}{
\begin{tabular}{c} Supercondutivity \\ \end{tabular} } & CSFFS & 0.1062 & 0.1009 & 0.0311 & 0.0263 & 0.0789 & 0.0300 & 0.0346 & 0.0343 & 0.0526 & 0.0625 \\  & XGBFS & 0.1800 & 0.1009 & 0.0414 & 0.0245 & 0.0698 & 0.0365 & 0.0480 & 0.0420 & 0.0260 & 0.0544 \\  & RFHFS & 0.0112 & 0.1009 & 0.0349 & 0.0232 & 0.0805 & 0.0313 & 0.0508 & 0.0444 & 0.0273 & 0.0523 \\  & FSNT & **0.0110** & **0.1009** & 0.0450 & **0.0225** & **0.0687** & 0.0428 & 0.1436 & 0.0466 & 0.0391 & **0.0363** \\  & Baseline & 0.0494 & 0.1009 & 0.0375 & 0.0260 & 0.0748 & 0.0321 & 0.0524 & 0.0331 & 0.0408 & 0.0484 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: RMSE values of datasets based on different feature selection methods. RFHFS, while CSFFS has a higher value than the Baseline. In the SVM regression algorithm, only FSNT and XGBFS have lower RMSE values than the Baseline, with FSNT having the lowest value. Under the MLP regressor regression algorithm, the RMSE value of the FSNT algorithm is significantly lower compared to CSFFS, XGBFS, and RFHFS, and it is the only algorithm with a lower value than the Baseline. Overall, the proposed FSNT algorithm performs exceptionally well across all datasets and exhibits clear advantages. The RFHFS algorithm is the only competitor to the FSNT algorithm. The FSNT algorithm achieves an average reduction of 54.02% in the RMSE value for regression prediction by selecting the regression algorithm with the lowest RMSE in the dataset and averaging their results.