# Machine Learning Ship Classifiers for Signals from Passive Sonars
## 6 Discussion
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline
**Model** & **0** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** \\ \hline Logistic One \(\times\) One & 29 & 0 & 1 & 3 & 15 & 384 & 6 & 14 & 2 & 4 \\ Logistic One \(\times\) Rest & 0 & 1 & 29 & 15 & 384 & 6 & 3 & 9 & 14 & 4 \\ SVM One \(\times\) One & 29 & 0 & 1 & 3 & 14 & 15 & 385 & 2 & 13 & 8 \\ SVM One \(\times\) Rest & 29 & 3 & 4 & 2 & 138 & 128 & 129 & 130 & 131 & 132 \\ MLP One \(\times\) One & 29 & 1 & 0 & 14 & 132 & 15 & 8 & 234 & 11 & 45 \\ MLP One \(\times\) All & 29 & 1 & 0 & 14 & 132 & 15 & 13 & 316 & 4 & 234 \\ LDA & 0 & 384 & 3 & 1 & 29 & 150 & 386 & 385 & 176 & 14 \\ XGBoost & 29 & 0 & 3 & 6 & 384 & 371 & 15 & 13 & 386 & 4 \\ Ridge & 0 & 1 & 2 & 10 & 6 & 5 & 28 & 4 & 59 & 18 \\ KNN & – & – & – & – & – & – & – & – & – & – \\ \hline
**Model** & **10** & **11** & **12** & **13** & **14** & **15** & **16** & **17** & **18** & **19** \\ \hline Logistic One \(\times\) One & 386 & 8 & 17 & 10 & 9 & 383 & 19 & 74 & 51 & – \\ Logistic One \(\times\) Rest & 28 & 17 & 8 & 176 & 11 & 13 & 59 & 20 & 385 & – \\ SVM One \(\times\) One & 9 & 384 & 163 & 33 & 34 & 16 & 164 & 4 & 18 & – \\ SVM One \(\times\) Rest & 133 & 134 & 135 & 136 & 137 & 399 & 139 & 127 & 141 & – \\ MLP One \(\times\) One & 316 & 13 & 368 & 321 & 4 & 133 & 230 & 359 & 395 & – \\ MLP One \(\times\) All & 8 & 11 & 117 & 359 & 45 & 7 & 134 & 395 & 133 & – \\ LDA & 99 & 13 & 53 & 97 & 15 & 186 & 68 & 293 & 59 & – \\ XGBoost & 73 & 138 & 139 & 144 & 137 & 140 & 141 & 136 & 142 & – \\ Ridge & 9 & 19 & 371 & 20 & 385 & 125 & 175 & 61 & 27 & – \\ KNN & – & – & – & – & – & – & – & – & – \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Results by classification method for features 0 to 19. To evaluate the effectiveness of the proposed XGBoost approach with dimensionality reduction techniques (Lofar, PCA, NLPCA), it is instructive to compare the classification accuracy with state-of-the-art methods based on Convolutional Neural Networks (CNNs). Previous studies that employed CNNs for similar SoNaR or signal classification tasks report accuracies typically ranging from 85% to 98% depending on the dataset and preprocessing strategies [35; 36]. For example, [PERSON] et al. [36] achieved an accuracy around 94% in related classification problems. In comparison, the XGBoost models in this work achieved mean accuracies up to 99.7% (see Table 11), which is competitive with or superior to many CNN-based approaches. This result highlights that despite the CNN's powerful automatic feature extraction, well-tuned gradient boosting on dimensionally reduced features can provide an excellent classification performance with a potentially lower computational cost [13]. Table 14 summarizes this comparison. This comparison suggests that gradient-boosting methods remain a strong alternative to deep learning models for SoNaR signal classification, particularly when computational efficiency is a concern. Among the evaluated models, XGBoost emerged as a top performer, offering an optimal balance between the classification accuracy and computational efficiency. Its capacity to handle complex multiclass problems--especially under decimated data conditions--makes it a compelling choice for maritime applications where computational resources may be limited. LDA, despite its linear nature, also demonstrated a strong performance, particularly in scenarios that required model interpretability and a low overhead. These findings highlight that XGBoost and LDA are valuable and underexplored alternatives to more common models in the ship classification domain. Their effectiveness suggests that further research should explore their adaptation and optimization across other maritime acoustic tasks, especially in large-scale and operationally diverse environments. It is important to underscore that the dataset used in this study contains classified acoustic signatures from Brazilian Navy vessels, forming part of a restricted military intelligence database.