# Machine Learning-Based Tools for Wind Turbine Acoustic Monitoring
## 3 Results and Discussion
In Table 2, for each feature contained in the data frame, the mean, median, maximal, and minimal importance of the one-third frequency components are shown. To facilitate the identification of the components that contribute the most to the discrimination between the operating conditions, in Table 2 the results have been sorted in descending order starting from the top (mean value). In this sorting, the column that shows the average value of importance, determined by the selection of characteristics, was used. It can be seen that for the first three components in the frequency range from 63 Hz to 400 Hz, the average of the importance of the variable is greater than 48. This  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Frequency** & **Mean** & **Median** & **Min** & **Max** \\ \hline
63 & 79.75986 & 78.79704 & 75.02274 & 86.74699 \\
80 & 52.25725 & 52.61418 & 48.52443 & 55.3532 \\
400 & 48.57361 & 49.25013 & 45.0459  
indicates that these characteristics are the ones that provide the greatest contribution to the discrimination between operating conditions. To obtain an overview and to better appreciate the results, a diagram is presented in Figure 7. Analyzing Figure 7 and Table 2, we can easily identify the variables that recorded the highest values of importance. If the first 10 returned variables (average > 46) are evaluated, it can be seen that they are within the 63-1600 Hz frequency range, with the addition of the 5 kHz frequency. To further reduce the calculation cost and avoid overfitting, we decided to further reduce the number of features selected to take only the first 10 among those that recorded the highest values on average. The variables selected by [PERSON]'s method represent the input variables for a support vector machine-based model implemented to detect the operating conditions of the wind turbines. This model creates an SVM classifier to classify the wind turbine operating conditions. The following SVM architecture was implemented:  
* Input data: The first 10 one-third frequency components returned by the feature selection method;
* Radial basis function (RBF) kernel;
* Multiclass classification (four classes). In the training phase, the algorithms based on SVMs use labeled data; therefore a correspondence between the input values and the relative output values associated with them can be established. In our case, each occurrence related to a combination of one-third frequency components was associated with a value of the relative class. The training aims to identify a set of weights w, one for each characteristic, of which the linear combination predicts the value of the class. This discourse of weight regulation is common with the training of algorithms based on artificial neural networks. Compared to artificial neural networks in SVMs, the optimization of margin maximization is adopted in order to reduce the number of non-zero weights. The results obtained in this way correspond to the characteristics that count in the separation of classes through the identification of  
Figure 7: Attribute importance for all the features (one-third frequency components) determined by the Boruta algorithm. For each frequency, the values of the statistics shown in Table 2 are reported: mean, median, min, and max. In this way, it is possible to obtain a graphical comparison of the importance that each feature assumes in the classification of wind farm operation. a hyperplane. These non-zero weights correspond to the support vectors because they identify the separation hyperplane [38; 39]. Before proceeding with the model training, it is necessary to split the data. The sampling methodology used for the subdivision of data can have a significant effect on the quality of the subsets used for training and testing. Poor data splitting can result in inaccurate and highly variable model performance [40]. To evaluate the generalization capacity of the model, the data were divided into two randomly selected groups:  
* A training set equal to 70% of the available observations (45,402 samples).