# Machine Learning Ship Classifiers for Signals from Passive Sonars
## 6 Discussion  
The results presented in this paper demonstrate that machine learning algorithms, particularly eXtreme Gradient Boosting (XGBoost) and Linear Discriminant Analysis (LDA) could effectively classify passive SoNaR signals into multiple ship classes using real-world data collected under operational maritime conditions. While prior works have reported high classification performances using synthetic or limited datasets [12; 22], our research advances the field by evaluating a broader range of models on a multiclass dataset acquired in authentic naval scenarios. All experiments were implemented using open-source Python libraries (e.g., scikit-learn, XGBoost), and all preprocessing steps, hyperparameter settings, and cross-validation protocols are explicitly documented to support reproducibility. These procedures ensure that the methodology can be replicated by researchers with access to comparable data. Table 13 presents the spectral bands selected by different classifiers using Lofar without decimation, where each value corresponds to a specific frequency bin out of a total of 400, uniformly distributed from 0 to 6 kHz. This means that each unit represents a 15 Hz  
Figure 9: Mean accuracy (\(\tilde{Acc}\)) for each model using Lofar, PCA, and NLPCA datasets, both with and without decimation. bandwidth. For example, bin 100 corresponds to a central frequency of 1.5 kHz, while bin 400 corresponds to 6 kHz. From the analysis, we observed that most classifiers tended to select features concentrated in the lower half of the spectrum (up to bin 200, or 3 kHz). This suggests that the most discriminative information for many models was located in the lower-frequency bands. In particular, classifiers such as ridge regression, logistic regression (one vs. one and one vs. rest), and LDA showed a strong focus on frequency bands below 1.5 kHz, indicating a preference for low-frequency features. This reinforced the suitability of Lofar signals for classification tasks. On the other hand, more complex or nonlinear models, such as the Multi-Layer Perceptron (MLP) and XGBoost, exhibited a broader and more dispersed selection across the frequency range. These models also frequently selected high-frequency bands above bin 300 (4.5 kHz), including values close to the maximum (e.g., 395, 399), suggesting that they were capable of extracting useful discriminative features from the higher end of the spectrum. Interestingly, the SVM one \(\times\) rest classifier selected a contiguous range of bins from 128 to 141, indicating a narrow band of interest centered around 2 kHz. This concentrated selection suggests that SVM with the one \(\times\) rest strategy is particularly sensitive to specific spectral regions. Additionally, Table 13 shows that KNN did not select features directly but instead relied on the distance between data points for classification. In summary, the simpler models tended to focus on the lower frequencies, whereas more complex classifiers, such as MLP and XGBoost, explored the entire spectral range, including higher frequencies. This diversity in frequency band utilization highlights how different learning models may prioritize distinct spectral characteristics for classification tasks and can be synchronized with the transducer that acquired the signal.