## 3 Results and Discussion
### Determine the Optimal Features Spaces  
The optimal features were selected from the six species using the Wrapper method. To determine optimal feature spaces, the XGBoost algorithm was first used to rank the features according to their importance values from minimum to maximum. Due to the fact that genomic sequence information encoding produces a large number of redundant and discrete features, it causes generous duplicate importance values. Hence, secondly, we regarded the feature importance values as thresholds for feature selection, and features with an importance value less than the threshold were discarded. The subset of features was continuously selected from the original features by thresholds, and it was evaluated according to the performance of XGBoost. As shown in Figure 3, the accuracy of 10-fold cross-validation varied as the features' threshold ranking was added in six species. Take the _E.coli_ dataset, for example, when the threshold ranking was 67, the performance of XGBoost was the highest when the shape corresponded to the (776, 194); in other words, when the threshold ranking was 67, the feature dimension was reduced to 194, and the XGBoost had the highest performance in the _E. coli_ dataset of 776 samples. Figure 3: Accuracies of the proposed features on datasets from _Caenorhabditis elegans_, _Drosophila melanogaster_, _Arabidopsis thaliana_, _Escherichia coli_, _Geolalkilbacter subterraneus_, and _Geobacter pickeringui_ as the number of selected features increases. This study compared the performance of the optimal features (after feature selection) and the original features (before feature selection) to verify whether the proposed feature selection strategy could improve the prediction performance. These two features were trained on the same dataset by the XGBoost algorithm and then evaluated using 10-fold cross-validation. Table 2 shows results of all the metrics (ACC, MCC, SP, SN). In all six species, the optimal features outweigh the original features to varying degrees. The results show that an average accuracy improvement of 1.7-4.2% was achieved using feature optimization compared to the original features; the MCC improved by an average of 2.7-9.8%. In Table 2, one can also intuitively see that the feature size of the six species was greatly reduced, especially in _A. thaliana_, where the feature size was reduced from 2628 dimensions to 184 dimensions. Again, a visual comparison was made between the original features and the optimal features, as shown in Figure 4.  
The optimized feature space described above is proven to effectively improve performance. However, how the learned features contribute to performance improvement is unknown. Here, this study further explored the change from the original feature space to the optimal feature space distribution to understand how features contribute to performance improvement. Hence, this study used T-distributed Stochastic Neighbor Embedding (t-SNE) technology [28] to reduce the dimensionality of the feature space to visualize the feature space. Figure 5 shows the three-scale datasets before and after determining the optimal features. We can observe the difference between the original features and the optimized features. Before optimizing features, the distribution between positive and negative samples is relatively scattered, but after optimizing featu