## 5 Experimental Results  
### Exploring the Impact of Feature Discarding Percentages on fasTNT Performance  
In this section, we evaluate the performance of fasTNT by varying the percentage of features discarded (\(\
u\%\)) with the lowest feature importance values from the ranked word list (\(W_{R}\)). The \(\
u\%\) values tested are 0% (no reduction), 10%, 20%, 30%, 40%, 50%, 60% and 70%. As shown in Figures 9-12, varying the \(\
u\%\) values results in F1-score performance improvements across the accumulated top-ranked topic clusters (i.e., the top-ranked topic cluster, top two ranked topic clusters, up to the top twenty ranked topic clusters (i.e., the top-ranked topic cluster, top two ranked topic clusters, up to the top twenty ranked topic clusters) over when no feature reduction is performed at all., after which no feature reduction is performed at all).  
Figure 10: F1-score performance of faSTNT across various feature-discarding percentages (\(\%\)) when utilizing the LitCovid dataset. The circles on the line represent the number of accumulated topic clusters.  
Figure 9: F1-score performance of faSTNT across various feature-discarding percentages (\(\%\)) when utilizing the WOS-5736 dataset. The circles on the line represent the number of accumulated topic clusters.  
One interesting point is that when the feature-discarding rate (\(\
u\%\)) is set to 70%, the F1-scores across the aggregated top-ranked topic clusters are the highest within the utilized feature set size (\(W_{F}\)). This trend is consistently observed across all four datasets, indicating the effectiveness of discarding lower-importance features in enhancing the predictive power of topic clusters for better classification performance.  
Figure 11: F1-score performance of faSTNT across various feature-discarding percentages (\(\
u\%\)) when utilizing the MultiLabel dataset. The circles on the line represent the number of accumulated topic clusters.  
Figure 12: F1-score performance of faSTNT across various feature-discarding percentages (\(\
u\%\)) when utilizing the arXiv dataset. The circles on the line represent the number of accumulated topic clusters.  
However, the maximum F1-score attained by each feature-discarding rate varies among datasets. Specifically, the maximum performance is preserved at -97% for the WOS-5736 dataset and -91% for the LitCovid dataset, even as the feature-discarding rate (\(\
u\%\)) increases from 10% to 60%. This indicates the ability of faSTNT to effectively remove redundant and irrelevant features from the feature pool, thereby enhancing model efficiency. However, after \(\
u\%\) exceeds 60%, the maximum performance for both the WOS-5736 and LitCovid datasets starts to decrease, suggesting that overly aggressive feature reduction may discard useful features. In contrast, for the MultiLabel and arXiv datasets, the achieved performance remains at -85% and -93%, respectively, when setting \(\
u\%\) from 10% to 40%. However, after \(\
u\%\) exceeds 40%, the maximum performance attained starts to decrease gradually. This suggests that, for these datasets, a higher feature-discarding rate may start to remove features that are critical for maintaining optimal classification performance.  
This analysis highlights the importance of tuning the feature-discarding rate to balance removing redundant information and preserving critical features for optimal classification performance. The results underscore the necessity of dataset-specific tuning to achieve the best results, demonstrating the robustness and adaptability of faSTNT in feature selection and classification tasks.