## 4 Experiment Setup
### Analysis of Experimental Results of Classified Datasets with Different Feature Numbers  
Figure 2 displays the average classification accuracy achieved by five feature selection algorithms (CSFFS, XGBFS, RFHFS, Baseline, and FSNT) along with eight classification algorithms (KNN, SVM, decision tree, random forest, Gaussian naive Bayes, neural network, logistic algorithm, and AdaBoost) on the Student Archive dataset. The x-axis represents the number of selected features (K), while the y-axis represents the average precision, average F1 score, and average accuracy obtained by each feature selection method. The  
black horizontal line represents the values obtained without using any feature selection algorithm. The comparison includes the FSNT algorithm and the three other feature selection algorithms, ranging from selecting 4 to 20 features. For the Student Archive dataset, the FSNT algorithm outperforms the other three algorithms in terms of F1 score, accuracy, and precision when k \(\leq\) 8. It achieves the maximum values at k = 20, surpassing XGBFS in F1 score and slightly trailing behind the CSFFS method in precision. Moreover, the FSNT algorithm also outperforms other methods in accuracy. Regarding the explanatory variance ratio chart for student achievement, the x-axis represents the number of selected features (k), while the y-axis represents the explanatory variance ratio obtained by each feature selection method. Both the CSFFS and FSNT methods exhibit significant superiority over the other two methods when k = 16. When the total number of features is 36, the FSNT method reaches a variance ratio exceeding 90% at k = 20, indicating the retention of the most information. Figure 3 illustrates the feature selection algorithm ranging from 4 to 20 features. For the TCGA Info with Grade dataset, the FSNT algorithm initially falls behind the other three algorithms in terms of F1 score, accuracy, and precision from K \(\geq\) 6 to 16. However, after k = 16, the FSNT method surpasses the CSFFS and RFHFS methods in F1 score, accuracy, and precision. At k = 20, all four methods exhibit similar performance across all evaluation criteria. Figure 2: Performance of different feature selection methods on Student Archive dataset. (**a**) F1; (**b**) precision; (**c**) accuracy; (**d**) dataset variance. On the explanatory variance ratio chart of the dataset, the CSFFS and FSNT methods outperform the other two methods when k = 8. Subsequently, the FSNT method begins to trail behind the CSFFS method until k = 20, while all methods maintain a variance ratio above 90%. Figure 4 illustrates the feature selection algorithm ranging from 3 to 15 features. For the Student-mat dataset, the FSNT algorithm achieves its highest F1 score at k = 6 before decreasing. At k = 9 and k = 12, the FSNT algorithm performs slightly lower than the CSFFS method but significantly outperforms the XGBFS method. At k = 15, the FSNT algorithm, as Baseline, significantly surpasses the other three methods. In contrast, the XGBFS method consistently lags behind the other methods from k = 3 to k = 20. The RFHFS method initially decreases to its lowest value at k = 6, then gradually rises and approaches the performance of the CSFFS method. The CSFFS method consistently outperforms the XGBFS and RFHFS methods, reaching its peak at k = 9 and k = 12 before decreasing. Figure 3: Performance of different feature selection methods on TCGA Info with Grade dataset. (**a**) F1; (**b**) precision; (**c**) accuracy; (**d**) dataset variance. Regarding accuracy, all methods exhibit similar trends to the F1 score except for the FSNT method. The FSNT method achieves accuracy equivalent to Baseline at k = 3 and reaches its peak at k = 6, significantly surpassing the performance of the Baseline. For precision, except for the CSFFS method, all other methods peak at k = 6, significantly outperforming the performance of the Baseline, and then decrease. The CSFFS method reaches its peak at k = 12.