# Deep-Learning-Based Identification of Broad-Absorption Line Quasars
## 4 Results and Discussion
The models using ISOMAP showed more significant performance declines, with an accuracy of 94.2% for the training data and 94.5% for the testing data. Although ISOMAP was effective in preserving the structure of the overall variety, its computational complexity and the nature of the data may have precluded its ability to fully grasp the complex relationships between the different quasars. Despite the accuracy of 95.8% obtained for all test data, the recall rate was 93.0% and the F1 score was 94.4%, which indicated that the model had more difficultly in correctly identifying all positive cases (BAL stars), resulting in a higher rate of false negativity (209 FNs for all the test data). Although commonly used for data visualization, the t-SNE algorithm performed the least well for this classification task, with 91.1% accuracy for the training set and 90.6% for the testing set. The model had a recall rate of 88.3% and an F1 score of 90.4% for the test dataset. As shown by the relatively high number of false negatives (351 FNs for the test dataset), the model had more difficulty in identifying positive cases. The fact that t-SNE focused on preserving local structures without maintaining overall relationships may have limited its ability to extract the most relevant features for classification. This was also reflected in the higher number of false positives and false negatives, indicating that the model had difficultly distinguishing between the two categories of quasars (BAL and non-BAL). The LLE focused more on preserving local relationships, allowing for a better understanding of the patterns and features of the dataset. Compared to ISOMAP, LLE focused more on local structure, required less computing power, and was suitable for larger datasets. On the other hand, ISOMAP focused on the global variety structures and may have required more computing resources, especially for large datasets, making it more suitable for cases where there were large distances between the data points or global schemas. Overall, the experimental results emphasize the importance of choosing an appropriate method for extracting features for a reduction in a dimension of a deep learning model. The ability of PCA to capture global patterns in the data allowed for more accurate classification, while the other approaches, particularly t-SNE, had difficulty maintaining the necessary balance between representing the local and global features. Since PCA showed the best performance, we applied the features by PCA as the inputs for a CNN model. In the CNN-based classification task, we demonstrated the effectiveness of the deep learning methods for spectral data classification, achieving up to 99.1% accuracy on the test dataset, as shown in Table 4. This showed the strong capability of deep learning models in distinguishing between BAL quasars and non-BAL quasars. Based on using PCA for the dimensionality reduction, Table 4 compares the performance of our best deep learning model with the XGBoost model previously developed by [PERSON] et al. [20]. Our model outperformed [PERSON]'s model in both accuracy and classification metrics for distinguishing between BAL and non-BAL quasars. Overall, this research yielded promising results in using deep learning methods to differentiate between BAL quasars and non-BAL quasars. While the various dimensionality reduction methods differed in how they captured data features, patterns, and computational costs, optimizing deep learning models played a critical role in achieving high accuracy in the classification tasks.