## 4 Experiment Setup
### Analysis of Experimental Results of Different Datasets with Different Feature Selection Methods
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Datasets**} & **Feature Selection Method** & \multirow{2}{*}{**KNN**} & \multirow{2}{*}{**SVM**} & \multirow{2}{*}{**DT**} & \multirow{2}{*}{**RF**} & \multirow{2}{*}{**Ada**} & \multirow{2}{*}{**LA**} & \multirow{2}{*}{**GNB**} & \multirow{2}{*}{**NN**} \\  & CSFFS & 0.7128 & 0.6658 & 0.6991 & 0.6639 & 0.6982 & 0.6658 & 0.7972 & 0.8016 \\  & XGBFS & 0.7457 & 0.6658 & 0.7098 & 0.7085 & 0.6610 & 0.6658 & 0.7620 & 0.8355 \\  & RFHFS & 0.7233 & 0.6658 & 0.7147 & 0.6648 & 0.7223 & 0.6658 & 0.8070 & 0.6630 \\  & FSNT & 0.6951 & **0.7158** & 0.6658 & 0.6658 & 0.6542 & **0.6658** & **0.8172** & 0.7858 \\  & Baseline & 0.7371 & 0.6658 & 0.7265 & 0.6648 & 0.6768 & 0.7137 & 0.7618 & 0.7403 \\ \hline \multirow{4}{*}{Student Archive} & CSFFS & 0.6820 & 0.7169 & 0.6849 & 0.7487 & 0.7245 & 0.7318 & 0.6962 & 0.7450 \\  & XGB & 0.6930 & 0.7045 & 0.6773 & 0.7427 & 0.7072 & 0.7184 & 0.6894 & 0.7512 \\  & RFHFS & 0.6475 & 0.7036 & 0.6848 & 0.7497 & 0.7240 & 0.7275 & 0.6712 & 0.7452 \\  & FSNT & 0.6814 & 0.7103 & **0.7005** & **0.7498** & **0.7269** & 0.7025 & **0.7081** & 0.7227 \\  & Baseline & 0.6348 & 0.7129 & 0.6965 & 0.7540 & 0.7244 & 0.7365 & 0.6906 & 0.7371 \\ \hline \multirow{4}{*}{TCGA Info with Grade} & CSFFS & 0.8446 & 0.7470 & 0.8050 & 0.8487 & 0.8660 & 0.8867 & 0.8540 & 0.8576 \\  & XGB & 0.8446 & 0.7470 & 0.7867 & 0.8471 & 0.8660 & 0.8867 & 0.8540 & 0.8924 \\ \cline{1-1}  & RFHFS & 0.8199 & 0.7420 & 0.8145 & 0.8546 & 0.8660 & 0.8867 & 0.8509 & 0.8867 \\ \cline{1-1}  & FSNT & 0.8199 & **0.7420** & **0.8166** & 0.8437 & **0.8660** & **0.8867** & **0.8540** & **0.8924** \\ \cline{1-1}  & Baseline & 0.8214 & 0.7424 & 0.8101 & 0.8336 & 0.8660 & 0.8867 & 0.8476 & 0.8924 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Precision values of datasets based on different feature selection methods. and AdaBoost classification algorithms, the advantages of the FSNT algorithm are not obvious, but slightly better than CSFFS, XGBFS, and RFHFS. Under the decision tree classification algorithm, the notes algorithm is 1.57% higher than RFHFS, 1.56% higher than CSFFS, and 2.32% higher than XGBFS. Compared with other algorithms, the notes algorithm has advantages. Under Gaussian naive Bayes classification algorithm, the notes algorithm is 3.69% higher than RFHFS, 1.19% higher than CSFFS, and 1.87% higher than XGBFS. In the Student Archive dataset, the notes algorithm is significantly higher than the RFHFS algorithm, chi square filter, and XGBFS. For the TCGA Info with Grade dataset, CSFFS, XGBFS, RFHFS, and FSNT achieved the same optimal precision under the SVM classification algorithm, logistic algorithm, and AdaBoost classification algorithm. Under the neural network classification algorithm, XGBFS and FSNT achieved the same optimal precision, slightly higher than RFHFS by 0.57% but significantly higher than CSFFS by 3.47%. In the decision tree classification, the precision obtained by FSNT is higher than that of the other three algorithms, which is significantly higher than that of the XGBFS algorithm by 2.99%, slightly higher than that of CSFFS and RFHFS, and also higher than the Baseline, with the best performance. In the TCGA Info with Grade dataset, the FSNT feature selection algorithm is not obvious but it still has advantages. In general, the proposed FSNT algorithm performs better than other algorithms on average. Two of the three datasets have obvious advantages. The only algorithm competitive with the FSNT algorithm is the RFHFS algorithm. The experiment shows that the FSNT algorithm performs better in the student performance dataset and the Student Archive dataset than in the TCGA Info with Grade dataset. It can be concluded that the FSNT algorithm is more suitable for small sample datasets. In the student performance dataset, the highest classification precision value was 81.72%, which was determined by FSNT and Gaussian naive Bayes.