## 4 Experiment Setup
### Analysis of Experimental Results of Different Datasets with Different Feature Selection Methods
In the Student Archive dataset, the highest classification precision value was 75.12%, which was determined by XGBFS and neural networks. In the TCGA Info with Grade dataset, the highest classification precision value was 89.24%, which was determined by FSNT and neural networks. When using RMSE as the evaluation index for regression, a smaller difference between the predicted and real values indicates better performance. The bold font in Table 4 represents the RMSE values that are the same or smaller compared to FSNT and other feature selection algorithms. The performance of four feature selection methods, namely CSFFS, XGBFS, RFHFS, and FSNT, is compared with a feature selection algorithm not used as a Baseline, using 10 regression algorithms. In the dataset, when the number of features reaches 30, it can be observed from the above analysis that the explained variance ratio exceeds 80%. However, despite increasing the number of features, the explained variance ratio does not show significant improvement. Therefore, all these feature selection methods select 30 features. Table 4 presents a summary of the RMSE values for the three datasets across 10 regression algorithms, which include linear regression, elastic net regression, random forest, extra trees, SVM, gradient boosted, decision tree regression, AdaBoost regression, Gaussian process regression, and MLP regression. The RMSE values are reported with four decimal places. The bold value in each row of the table indicates that FSNT achieves the same or better performance compared to the other three methods. In Table 4, for the Student-por dataset, FSNT achieved the lowest RMSE under the elastic net regression algorithm, slightly surpassing the RMSE values of other feature selection algorithms and the Baseline by 1.3%. The FSNT algorithm exhibits a significantly lower RMSE value compared to CSFFS and RFHFS under the random forest regression algorithm, by 4.48%. The FSNT algorithm demonstrates superior regression performance compared to CSFFS and RFHFS by 2.57% under the SVM regression algorithm, and it also slightly outperforms RFHFS and the Baseline. The FSNT algorithm exhibits significant advantages under the decision tree regression algorithm. Its RMSE value is 14.13% lower than that of RFHFS, 10.77% lower than that of CSFFS, and 2.43% lower than that of XGBFSand the Baseline. The RMSE value of the FSNT algorithm remains the lowest among all feature selection algorithms under the Gaussian process regression algorithm, with a difference of approximately 2.8%. For the student performance dataset, FSNT achieved the lowest RMSE under the elastic net regression algorithm, slightly surpassing the RMSE values of other feature selection algorithms and the Baseline by 1.3%. The FSNT algorithm exhibits a significantly lower RMSE value compared to CSFFS and RFHFS by 4.48% under the random forest regression algorithm. The FSNT algorithm demonstrates superior regression performance compared to CSFFS and RFHFS by 2.57% under the SVM regression algorithm, and it also slightly outperforms RFHFS and the Baseline. The FSNT algorithm exhibits significant advantages under the decision tree regression algorithm. Its RMSE value is 14.13% lower than that of RFHFS, 10.77% lower than that of CSFFS, and 2.43% lower than that of XGBFS and the Baseline. The RMSE value of the FSNT algorithm remains the lowest among all feature selection algorithms under the Gaussian process regression algorithm, with a difference of approximately 2.8%. For the Online News Popularity dataset, the RMSE value of FSNT, along with the other three feature selection algorithms, is the same as the RMSE of the Baseline under the elastic net regression algorithm and SVM regression algorithm. The RMSE value of the FSNT algorithm is slightly lower than that of CSFFS, XGBFS, and RFHFS under the random forest regression algorithm. The FSNT algorithm demonstrates significant advantages under the AdaBoost regressor regression algorithm.