## 3 ML Algorithm Steps
### Feature Selection  
Conductive to prevent the classification algorithm from adapting to the observed data rather than learning the features, i.e., overfitting, the number of features that are intended to be used in the classification procedure should be proportional to the number of observations. A high number of features in the feature vector may improve the training stage, but the accuracy of the testing step can diminish sharply [48]. In fact, since there are non-informative features among the extracted items, they can confuse the network and detrimentally impact both the training and testing stages. As a result, it is imperative to identify and prioritize the most salient and informative features. A variety of feature selection methods are available, ranging from metaheuristic techniques to more straightforward statistical tests such as the \(t\)-test [49]. On the one hand, as far as the R-Classification is concerned, it was accomplished using two different sensor configurations. Feature selection was executed independently for the S2-S3 and S4-S5 combinations, as will be demonstrated in the following: the features that proved suitable for the complex S2-S3 configuration did not exhibit the same efficacy for S4-S5. The feature selection phase was carried out using the Sequential Forward Selection (SFS) technique. This way, features are incrementally added to an empty feature vector until the rest features do not diminish the criteria. A custom criterion was identified with the intention of setting the maximum number of iterations to infinity, and the 5-fold cross-validation method was introduced as the validation procedure. Additionally, a relative objective function with a termination tolerance of \(1\times 10^{-6}\) was established. After the feature selection procedure, the selected feature vector for the combination of sensors S2-S3 contains 9 features as presented in Table 3.  
Similarly, the same process--the SFS--was carried out for the pairing S4-S5, with the exception that in this instance, 12 features were chosen using the same feature selection algorithm criteria. The features that were chosen for the S4-S5 combination are displayed in Table 4. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Number**} & **Name of the** & **Number** & **Name of the Feature** \\  & **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) \\
9 & & distortion & 24 & Mean value of the wavelet detail coefficients in level 1 (MDET1) \\  & & & level 1 (MDET1) \\
10 & Signal to noise & & \\  & 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 2: Extracted feature for each channel (sensor).