## 4 Experiments and Results
### Experimental Results
#### 4.3.3 Sensitivity Analysis of Attribute Screening Factors  
In the feature space reconstruction stage, sensitivity analysis is performed on the attribute screening factors to maximize the retention of helpful information in the features. For each dataset, five sets of feature space scales are obtained according to five attribute screening factors (\(\beta\in[0.1,0.2,0.3,0.4,0.5]\)), and the training is performed on the decision tree classifier separately; based on the experimental results, the attribute screening of different datasets on the decision tree classifier is analyzed using four model evaluation metrics (accuracy, recall, F1-score, precision). The sensitivity of the factors is analyzed, and the best attribute screening factor for the dataset is determined according to the sensitivity analysis results. The spatial scale of the features under the constraint of the optimal attribute screening factor is the spatial dimension to which the subsequent PCA dimensionality reduction should be reduced.  
The results of the attribute screening factor sensitivity analysis visualization are listed in Figure 3a-d, respectively (taking the AsianReligionsData, Arrhythmia, Ad, QSAR dataset as an example). The heatmaps visualize the changes and comparisons of the metrics, and the shades of the color blocks indicate the magnitude of the values of the metrics (Accuracy, Recall, F1-score and Precision) for different attribute screening factors. Darker colors (usually toward the darker end) indicate higher values; lighter colors indicate lower values.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Dataset** & **DT** & **RF** & **AdaBoost** & **GB** & **ET** & **SVM** \\ \hline AsianReligionsData & 0.7458 & 0.8475 & 0.5932 & 0.8220 & 0.8898 & 0.7881 \\ Arrhythmia & 0.6484 & 0.7582 & 0.6044 & 0.6923 & 0.7363 & 0.6044 \\ CNAE & 0.8333 & 0.8843 & 0.4768 & 0.9074 & 0.9074 & 0.8935 \\ Gait & 0.1000 & 0.8000 & 0.2000 & 0.6000 & 0.9000 & 0.6000 \\ Ad & 0.9695 & 0.9787 & 0.9695 & 0.9664 & 0.9741 & 0.9207 \\ Parkinson & 0.8146 & 0.8675 & 0.8808 & 0.8940 & 0.8940 & 0.7417 \\ QSAR & 0.8669 & 0.8935 & 0.8846 & 0.8876 & 0.8964 & 0.8905 \\ QSAR\_oral & 0.96 & 0.9783 & 0.9988 & 0.9988 & 0.9900 & 0.9972 \\ REJAFADA & 0.9825 & 0.9900 & 0.9825 & 0.9875 & 0.9875 & 0.5965 \\ SCADI & 0.7143 & 0.8571 & 0.2143 & 0.7143 & 0.8571 & 0.8571 \\ Secom & 0.8694 & 0.9013 & 0.8822 & 0.9013 & 0.9013 & 0.9013 \\ Urban & 0.8333 & 0.8910 & 0.8889 & 0.8889 & 0.8941 & 0.6667 \\ \hline \end{tabular}
\end{table}
Table 3: Accuracy of training the model using all features in the dataset.  
Take (a) as an example to quantitatively analyze the performance of different indicators under the attribute screening factor. The sensitivity is calculated as follows:  
\[\text{Sensitivity}=\frac{\sum\Delta\text{metric}}{\text{n}-1} \tag{33}\]  
where \(\Delta\text{metric}\) is the difference in performance metrics between successive attribute screening factors, and \(n\) is the number of attribute screening factors.  
Accordingly, the sensitivity of all metrics on the AsianReligionsData dataset is obtained. The Accuracy, Recall, F1-score, and Precision sensitivities are 0.0263, 0.0237, 0.0194, and 0.0136, respectively. It shows that for the AsianReligionsData dataset, Accuracy and Recall energy metrics are more sensitive to the changes in attribute screening factors.