## 3 Materials and Methods
### Methodology
#### 3.2.2 Stage II: Classification  
Stage II of the proposed approach deals with the application of advanced ML algorithms as a tool for classification. The features identified after the application of NCA are fed into each of the classifiers to measure the efficacy of the selected features. Here, we used 11 state-of-the-art machine learning classifiers [12; 13], namely, Decision Tree (DT), Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), Linear Support Vector Machine (L-SVM), Quadratic Support Vector Machine (Q-SVM), Cubic Support Vector Machine (C-SVM), Gaussian Support Vector Machine (G-SVM), K-Nearest Neighbours (k-NN), Boosted Tree Ensemble (BSE), Bagged Tree Ensemble (BGE), and Subspace Discriminant Ensemble (SDE). Here, instead of using the generic algorithms, we tuned the hyperparameters of all the algorithms to provide optimal results. **A decision tree** is a tree structure that constructs classification or regression models. It progressively divides a dataset into smaller and smaller sections, while also developing an associated decision tree. The result is a tree containing leaf nodes and decision nodes [14]. In this study, we used a tuned decision tree, where we took the Gini Diversity Index as a split criterion, with a maximum number of 100 splits and fine tree type. **Linear Discriminant Analysis** is a linear classification model. Data from a D dimensional feature space are projected using LDA into a D' (D > D') dimensional space to increase variability across classes, while minimizing variability within classes. LDA works well while dealing with multiclass classification [15]. Here, in the case of linear discriminant analysis, we used the \"full\" covariance structure. **Quadratic Discriminant Analysis** is a generative model. According to QDA, each class is thought to have a Gaussian distribution. The fraction of data points that belong to the class is the class-specific prior. The average of the input variables that are part of the class makes up the mean vector particular to that class. Simply put, the covariance of the class-specific vectors makes up the class-specific covariance matrix [16]. In this work, we have used a \"full\" covariance structure in the case of quadratic discriminant analysis. **Linear Support Vector Machine** is a linear model that may be used to solve classification and regression issues. It can handle linear and non-linear problems and is useful for a wide range of practical applications. The concept of SVM is straightforward: The method draws a line or a hyperplane to divide the data into classes [17]. In this experiment, we tuned the hyperparameters of the linear SVM for the betterment of the classification results. To obtain the optimal results, we considered the \"Linear Kernel\" with an \"automatic\" kernel scaling, keeping the box constraint level as 1, and considering the multiclass method as one vs one. **Quadratic kernel-free non-linear support vector machine** (Q-SVM) is a quadratic decision function capable of separating data non-linearly. The geometrical margin is shown to be equal to the inverse of the gradient of the decision function's norm. The equation of the quadratic function is the functional margin. It is demonstrated that Q-SVM may be used in a quadratic optimization context [17]. Here, in this study, we tuned the hyperparameters of the SVM for the betterment of the classification results. To obtain the optimal results, we considered the \"Quadratic Kernel\" with an \"automatic\" kernel scaling, keeping the box constraint level as 1, and considering the multiclass method as one vs one. **Cubic support vector machine** (C-SVM): when dealing with a memory space constraint, C-SVM is an effective SVM approach because it locates a hyperplane in a multidimensional space that best separates the classes, whereas Q-SVM has low memory utilization for binary classification and high memory utilization for multiclass classification during its training phase.