# Advancing Taxonomy with Machine Learning: A Hybrid Ensemble for Species and Genus Classification
## 3 Materials and Methods
### Dataset with Simulated Undescribed Species
Let us examine some of them. * As is often the case in machine learning and deep Llearning, no single method consistently outperforms all others across the datasets tested in this study. Instead, the best results are achieved through an ensemble approach. This suggests that different methods capture distinct aspects of the data, and their combination allows for a more comprehensive extraction of information, ultimately leading to superior performance compared to individual methods. * It is important to highlight that methods relying solely on image-based features perform worse than those based on DNA barcoding. However, integrating both types of features--those extracted from images and those derived from DNA information--enhances the performance of a DNA-only classifier. * Even when compared to state-of-the-art methods across various datasets, our ensemble approach emerges as the top performer. This reinforces our confidence that, despite the complexity of combining different techniques, the proposed ensemble method is robust across diverse datasets. As a result, it serves as a strong baseline for other researchers working with combined image and DNA barcoding data. * Another notable observation comes from the plot in Figure 6. The proposed method demonstrates a substantial improvement over SVM, which is currently the most widely used classification technique; this highlights the potential usefulness of our system for the research community. Our method achieves impressive performance reaching 90% species-level accuracy and 90% genus-level accuracy; this is significantly higher than the results obtained with SVM. Moreover, these improvements have practical implications for expert naturalists, as they facilitate the identification of previously unknown species based on DNA barcoding and imaging with high accuracy. * Finally, an interesting result reported in Table 4 pertains to the Unseen dataset, where species identities are unknown. Notably, DNA barcoding performs the worst in this

Figure 6: SVM\({}_{f}\) (green line) vs. proposed ensemble (black line), with species accuracy (x-axis) versus genus accuracy (y-axis) obtained by varying the rejection threshold \(\tau\). dataset compared to the others tested. Nevertheless, even in this case, the ensemble method yields the best results. The fact that DNA barcoding does not significantly outperform image-based methods suggests that this dataset differs from the others. This further underscores the generalizability of the proposed approach. As previously depicted, the results clearly show the usefulness of the ensemble proposed in this work; obviously, the disadvantage of the ensemble is the higher computational power required to perform inference and training. So, these ensemble-based methods are certainly not suitable for real-time analysis on edge computing devices, and they require the ability to access a server where modern GPUs are available. This is not a problem in many applications, e.g., thanks to low-cost satellite connections such as Starlink, it is relatively easy to have access to the network, so the number of projects in which an ensemble can be used has been increasing in recent years. In contrast, in all those applications where classification must be carried out on an unconnected device, simply because of the need to reduce power consumption, these approaches are not the ideal choice, because of the computing power required. The experiments were conducted on the following hardware setup:

* Processor: Intel Core i5-12400 CPU (2.5 GHz, six cores);
* GPU: NVIDIA RTX 4070 (12 GB GDDR6X) for accelerated deep learning tasks;
* RAM: 32 GB DDR5;
* Programming Languages: Python 3.12.1 (for feature extraction tasks) and MATLAB 2024b (for wavelet transforms and classification). Deep Learning Framework: PyTorch 2.0, utilized for implementing and training the convolutional neural networks used for extracting features. LibSVM was used for support vector machines.