# Advancing Taxonomy with Machine Learning: A Hybrid Ensemble for Species and Genus Classification
## 3 Materials and Methods
### Dataset with Simulated Undescribed Species
It introduces residual connections, or skip connections, that allow gradients to flow directly through the network, bypassing one or more layers. This is achieved by reformulating the layers to learn a residual mapping \(F(\mathbf{x})=H(\mathbf{x})-\mathbf{x}\), where \(H(\mathbf{x})\) is the original mapping, and the output is \(F(\mathbf{x})+\mathbf{x}\). ResNet is highly effective for multi-class classification tasks. The network consists of stacked residual blocks, each comprising convolutional layers, batch normalization, and ReLU activations, with a skip connection that adds the input of the block to its output. The architecture scales to hundreds or thousands of layers while maintaining high performance. ResNet models are often initialized with weights pre-trained on the dataset ImageNet [43], leveraging features learned from over a million diverse images, a technique also known as transfer learning. This approach accelerates convergence, improves performance on downstream tasks, and is computationally efficient compared to training a deep network from scratch. Each net is trained for 10 epochs, with a batch size equal to 30, a learning rate 0.001, and stochastic gradient descent (SGD) for optimization. ## 4 Results

In this section, the different approaches are compared using the five datasets described in Section 3. The classification performance for the Badirli dataset (performance is reported in Table 2) and the two datasets proposed here, see Section 3.1 (performance is reported in Table 3) and Section 3.2 (performance is reported in Table 4), was assessed by the weighted species accuracy and the weighted genus accuracy:

Figure 2:4

where for class \(j\), \(y_{j}\) is the number of correctly classified patterns of class \(j\), \(n_{j}\) is the total number of patterns for that class, and \(n\) is the number of species or the number of genera. Notice that this is the same formula for the weighted species accuracy and the weighted genus accuracy. Instead, for the Beetle and Fish datasets, the standard accuracy is used as a performance indicator, as in the related literature; the performance is reported in Tables 5 and 6. The following methods are reported in this section:

* Bad, the method detailed in [16];
* Bad\({}_{D}\), the method, based only on DNA barcoding, detailed in [16];
* SVM\({}_{D}\), SVM trained using the features proposed in [16] to represent the DNA sequence;
* SVM\({}_{O}\), SVM trained using the features proposed in [16] to represent both the DNA sequence and image;
* SVM\({}_{N}\), SVM trained using the features proposed in this paper to represent both DNA sequence and image;
* SVM\({}_{f}\), sum rule between SVM\({}_{O}\) and SVM\({}_{N}\);
* DWT\({}_{O}\), an ensemble of 15 ResNet50 trained using the DWT approach coupled with the features proposed in [16] to represent both the DNA sequence and image;
* DWT\({}_{N}\), an ensemble of 15 ResNet50 trained using the DWT approach coupled with the features proposed in this paper to represent both the DNA sequence and image;
* DWT\({}_{f}\), sum rule between DWT\({}_{O}\) and DWT\({}_{N}\);
* DWT\({}_{O}\) + SVM\({}_{O}\), sum rule between DWT\({}_{O}\) and SVM\({}_{O}\);
* DWT\({}_{f}\) + SVM\({}_{f}\), sum rule between DWT\({}_{f}\) and SVM\({}_{f}\);
* eDNA, the method proposed in [44] for DNA barcoding classification;
* Proposed, the weighted sum rule among \(0.5\times\text{DWT}_{f}+1\times\text{SVM}_{f}+1\times\text{eDNA}\). Not all methods are reported for all datasets; for example, in the case of the Badirli dataset, since we do not have any images available, we cannot calculate the new features detailed in Section 3.4. In both the Fish and Beetle datasets, the performance of SVM with original features is low; to reduce computation time, we did not compute DWT\({}_{O}\) and, therefore, the method named \"Proposed\" for the Beetle and Fish dataset is given by the following: \(0.5\times\text{DWT}_{N}+1\times\text{SVM}_{N}+1\times\text{eDNA}\).