## 3 ML Algorithm Steps
### Feature Selection
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Number**} & **Name of the** & **Number of the Sensor** \\  & **Feature** & **Number** & **Name of the Feature** \\ \hline
1 & Shape factor & 16 & Maximum fractal length (MFL) \\
2 & Kurtosis & 17 & Logarithmic representation of Teager-Kaiser \\  & & & energy operator (LTKEO) \\
3 & Skewness & 18 & Spurious free dynamic range (SFDR) \\
4 & Peak value & 19 & Band power \\
5 & Crest factor & 20 & Amplitude of the first frequency harmonic \\
6 & Impulse factor & 21 & Frequency of the first frequency harmonic (FFFH) \\
7 & Clearance factor & 22 & Second root of the summation of the power \\  & & values of persistence spectrum (SRPVPS) \\
8 & Signal-to-noise & 23 & Mean value of the wavelet approximation \\  & ratio (SNR) & & coefficients in level 4 (MAPP4) \\  & Total harmonic & & Mean value of the wavelet detail coefficients in \\
9 & distortion & 24 & level 1 (MDET1) \\  & & & level 1 (MDET1) \\  & Signal to noise & & \\
10 & and distortion & 25 & Shannon entropy \\  & ratio (SINAD) & & \\  & Difference & \\  & absolute & & \\
11 & standard & 26 & Difference absolute mean value (DAMV) \\  & deviation value & & \\  & (DASDV) & & \\  & Logarithmic & & \\
12 & representation & & \\  & of DASDV & 27 & Waveform length \\  & (LDASDV) & & \\
13 & Variance & 28 & Teager–Kaiser energy operator (TKEO) \\
14 & Standard & 29 & Logarithm transformation of DAMV (LDAMV) \\  & deviation (SD) & & \\  & Mean absolute & & \\
15 & deviation & 30 & Median \\  & (MAD) & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: R-Classification: name of selected features for sensors pair S2-S3. For the S-Classification task, an analogous procedure employing SFS with the specified criteria led to the automatic selection of the most informative 13 features. The outcome of the S-Classification features selection process are presented in Table 5.  
To improve the representation of the results, the scatter plot of waveform length S3 and LDAMV S4 was graphed in Figure 13a; the scatter plot for the same features and for sensors 2 and 3 was plotted in Figure 13b. Since there is more overlap between classes in Figure 13b compared with Figure 13a, these two features can result in wrong classification. Figure 12: R-Classification: (**a**) two selected features; (**b**) two unselected features for sensors pair S4-S5. \begin{table}
\begin{tabular}{c c c} \hline
**Number** & **Name of the Feature** & **Number of the Sensor** \\ \hline
1 & DASDV & S2 \\
2 & DASDV & S3 \\
3 & Waveform length & S3 \\
4 & Waveform length & S4 \\
5 & Variance & S4 \\
6 & MAD & S3 \\
7 & MEL & S2 \\
8 & MFL & S5 \\
9 & TKEO & S3 \\
10 & TKEO & S4 \\
11 & LDAMV & S4 \\
12 & Median & S3 \\
13 & Median & S4 \\ \hline \end{tabular}
\end{table} 
Table 5: S-Classification: name of selected features for all sensors. Although there is some overlap in the graph, from Figure 13a it can be well appreciated that each S-Class covers a distinguishable area of the plot; moreover, a few numbers of outliers can be seen. It is worth noting that the presence of outliers in the dataset is acknowledged and retained, as their occurrence is inherent in real-world scenarios. Overall, this level of distinguishability is deemed acceptable for an experimental work where the environmental noise, potential human errors, and instrument limitation can all influence the outcome.