## 4 Experiments and Results
### Experimental Results
#### 4.3.4 Ablation Experiment  
We observe that for most datasets, the reconstruction of the weak feature space using different attribute screening factors primarily impacts the classifier's performance. Therefore, we conduct ablation experiments to explore the effectiveness of high-dimensional feature space techniques in classification tasks. (1) Ablation experiments based on feature space reconstruction scale  
We have conducted ablation experiments to explore the contribution of the feature space reconstruction scale to the model performance. Figure 4 shows the performance of the 12 UCI high-dimensional datasets under the initial classifier ensemble with the constraint  
Figure 3: Attribute screening factor sensitivity analysis visualization results (partial). (**a**) AsianReligionsData dataset. (**b**) Arrhythmia dataset. (**c**) Ad dataset. (**d**) QSAR dataset. of the attribute screening factor \(\beta\), demonstrating the effect of different attribute screening factors \(\beta\) on classifier performance. (The box-shaped rectangle represents the fluctuation range of the data, and the upper and lower boundaries of the rectangle represent the third quartile and first quartile of the corresponding accuracy, respectively. The solid red line represents the median of the accuracy of each classifier in the classifier ensemble sorted in order, and the blue dashed line represents the accuracy mean.) Figure 5 shows the visualization of the variation of the reconstructed features. Figure 4: Influence of attribute exclusion factor \(\beta\) on classifier performance, where _“*”_ represents an outlier point, the solid red line represents the median of the accuracy of each classifier in the classifier ensemble sorted in order, and the blue dashed line represents the accuracy mean. Under the constraint of the attribute screening factor \(\beta\), some of the features are optimized to be able to improve the model training accuracy to a certain extent; for example, for the AsianReligionsData dataset, the highest accuracy is only 0.8898 when the initial classifier set is trained using the original feature space in Table 2. In contrast, when \(\beta\) is taken to the value of 0.5, discriminative optimization is performed to the feature space, and the highest accuracy is up to 0.9062. The possible reason for this is that optimizing some of the noisy, weakly discriminative features is information unrelated to the current classification task, and when there are strong interrelationships or repetitive information between the features, there will be multiple covariance problems, and at this time, optimizing the features will eliminate the covariance and reduce the bias in estimating the importance indices between the features. Secondly, optimizing some features can reduce the degrees of freedom, shrink the model complexity, and reduce the risk of overfitting. Parts of the dataset for the box accuracy are more concentrated, for example, Ad, QSAR_oral, and Secom dataset. Take the Ad dataset as an example, its \(\beta=0.1\), and the accuracy of the six classifiers is as low as 0.9228, the highest is 0.9750, the fluctuation is small, but part of the dataset appeared to deviate from the data distribution of the \"outlier accuracy\". For example, when Adaboost is trained on the SCADI dataset, the highest accuracy is only 0.2555 when a different \(\beta\) is used to optimize the features, whereas the accuracy of other classifiers can reach 0.8860, which may be because some of the classifiers' structures and preferences are unable to obtain the detailed feature information that is most useful for the current task, and they are unable to deal with the high-dimensional data, which will affect the classifiers' performance. Therefore, we conduct ablation experiments to explore the effectiveness of high-dimensional feature space techniques in classification tasks.