# Signal Preprocessing for Enhanced IoT Device Identification Using Support Vector Machine
## 4 Discussion  
The classification performance of the SVM-based classifier under different RF signal preprocessing options is summarized in Table 7, which presents five key metrics: _diagnostical accuracy_, _effective accuracy_, _recall_, _specificity_, and _F1 score_. These metrics provide a comprehensive assessment of the classifier's ability to discriminate between classes and handle noisy or unbalanced data. The results further emphasize the critical role of RF signal preprocessing in improving the effectiveness of a classifier. The results show that the use of raw RF signals offers limited performance, with a _diagnostical accuracy_ of 36.28% and an _F1 score_ of only 33.02%, indicating a high susceptibility to noise in the original signals. Although the _specificity_ is high (96.63%), the low _recall_ (40.93%) suggests that the model struggles to detect positive cases, limiting its applicability. This contrasts significantly with RF signal preprocessing approaches based on signal peaks, which substantially improve performance, especially when considering features such as maximum or average values. Among the evaluated options, signal preprocessing using the maximum signal peaks stands out as the most effective approach, achieving a _diagnostical accuracy_ of 84.95%, an _effective accuracy_ of 100.00%, and an _F1 score_ of 82.32%. This is because maximum peaks capture key discriminative features, allowing the model to efficiently distinguish between different classes while reducing the impact of noise. In comparison, using minimum or average peaks also significantly improves performance--albeit at a slightly lower sensitivity, with a recall of 82.77%--demonstrating that extreme-value preprocessing is essential for optimizing feature extraction and maximizing the classifier's effectiveness. To assess the effectiveness and generalization capability of the proposed SVM-based classification scheme, we conducted a comparative evaluation using the Random Forest (RF) algorithm as a benchmark. The RF model was implemented using the TreeBagger function in MATLABTM, configuring an ensemble of 100 decision trees with out-of-bag (OOB) estimation enabled. This configuration allowed for an unbiased assessment of classification performance without the need for a separate validation set. To maximize information gain at each decision split, all predictors were considered during node splitting. Table 8 presents the classification accuracy obtained with both SVM and RF classifiers across different preprocessing strategies. While SVM consistently outperformed RF across all configurations, the performance gap was especially notable when signal normalization was applied. In particular, max-normalization yielded the highest classification accuracy for both classifiers, with SVM achieving 86.21% and RF reaching 78.09%. This suggests that preprocessing techniques significantly influence classifier effectiveness, especially for algorithms like SVM that are sensitive to feature scaling. \begin{table}
\begin{tabular}{l c c c c c} \hline
**Preprocessing** & _Diagnostical_ & _Effective_ & _Recall_ & _Specificity_ & _F1 Score_ \\  & _Accuracy_ [\%] & _Accuracy_ [\%] & **[\%]** & **[\%]** & **[\%]** \\ \hline Raw signal & 30.59 & 33.33 & 35.96 & 96.98 & 29.09 \\ Normalized raw signal & 51.06 & 75.00 & 53.25 & 97.89 & 49.76 \\ Mean-normalized raw signal & 84.29 & 95.83 & 87.39 & 99.32 & 83.65 \\ Max-normalized raw signal & 86.21 & 100.00 & 87.34 & 99.39 & 86.19 \\ Min-normalized raw signal & 84.70 & 95.83 & 85.06 & 99.33 & 84.15 \\ \hline \end{tabular}
\end{table} 
Table 7: Impact of different variants of the RF signal preprocessing applied. \begin{table}
\begin{tabular}{l c c c c c} \hline
**Classifier** & **Raw Signal** & **Normalized** & **Mean-Normalized** & **Max-Normalized** & **Min-Normalized** \\ \hline SVM & 30.59 & 51.06 & 84.29 & 86.21 & 84.70 \\ Random Forest & 39.57 & 61.18 & 73.77 & 78.09 & 73.77 \\ \hline \end{tabular}
\end{table}