## 2 Methodology
### Feature Selection by Boruta  
Feature selection keeps a great effect on the performance of machine learning algorithms. In the supervised algorithms, the identity of the labels is identified, and different feature extraction algorithms are applied to obtain the intrinsic set of features from the given data. In a practical scenario, while dealing with data, it is very common to have redundant and less information into the data. The main purpose of selecting set of features is to get rid of that redundant information and reducing the data-dimension. Generally, feature selection algorithms do this part. However, there are dimensionality reduction approaches also, by which the dimension of the data can be reduced. While both types of algorithms are applied for lessening the number of features, there is a critical difference. Feature selection algorithms just pick and ignore given features without transforming them. Therefore, dimensionality reduction algorithms changes feature into a lower dimension by both linear and non-linear data analysis. For the studies related to feature analysis, the feature selection algorithms mainly serve two purposes, i.e., (a) dimensionality reduction of the data and (b) finding out the important non-redundant attributes \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Property** & **Equation** & **Property** & **Equation** & **Property** & **Equation** \\ \hline F4 & \(\sqrt{\frac{1}{N}\sum\limits_{i=1}^{N}X_{i}^{2}}\) & F5 & \(\frac{1}{N}\sum\limits_{i=1}^{N}\left(\frac{X_{i}-\overline{X}}{\sigma}\right)^ {4}\) & F6 & \(\frac{1}{N}\sum\limits_{i=1}^{N}\left(\frac{X_{i}-\overline{X}}{\sigma}\right)^ {3}\) \\ \hline F7 & \(\frac{\frac{1}{N}\sum\limits_{i=1}^{N}\left(\frac{X_{i}-\overline{X}}{\sigma} \right)^{3}}{\frac{1}{N}\sum\limits_{i=1}^{N}\left[X_{i}\right]}\) & F8 & \(\frac{\frac{\max\left(\left|X\right|\right)}{\frac{1}{N}\sum\limits_{i=1}^{N} \left[X_{i}\right]}}{\frac{1}{N}\sum\limits_{i=1}^{N}\left[X_{i}\right]}\) & \\ \hline F9 & \(\sqrt{\frac{1}{N}\sum\limits_{i=1}^{N}F_{i}^{2}}\) & F10 & \(\frac{1}{N}\sum\limits_{i=1}^{N}\left(\frac{F_{i}-\overline{x}}{\sigma}\right)^ {4}\) & F11 & \(\frac{1}{N}\sum\limits_{i=1}^{N}\left(\frac{F_{i}-\overline{x}}{\sigma}\right)^ {3}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Numerical explanation of statistical features. Figure 3: Illustration of AE signal feature. from the data. For these reasons, in this study, the feature selection algorithm is considered over dimensionality reduction techniques. Feature selection algorithms have the nature to select only the non-redundant attributes from the data. If from a dataset, the analysis demands to find out all the relevant attributes rather than the non-relevant ones, feature selector algorithms cannot do that. These algorithms can pick only the non-redundant ones but miss those redundant attributes which are relevant and important. To solve this problem, the necessity of wrapper method arises [14; 15]. To solve this purpose, a wrapper-based approach is considered in this research [16]. [PERSON] first clones the given set of features to extend the provided information. These clones of features are identified as shadow features. After that, it rearranges the elements of the cloned shadow features to eliminate the correlations. Then, to validate the significance of features by the mean decrease impurity (MDI), it trains the shadow features with random forest (RF) classifier. MDI determines the significance of each cloned feature. The shadow feature containing the highest score of MDI is counted as the best. Now, to determine the importance of the provided feature attributes, the algorithm make a test with only the provided real features (excluding the cloned shadow features). For this objective, the Z score is considered. The algorithm evaluates whether the provided feature has a higher Z score than the maximum of its shadow feature. If the score is high, it is recorded in a vector known as hits.