## 5 Research Method
It sorts values for numeric attributes once, deals with missing values by splitting them into fractional instances using C4.5, and creates multiple trees in various iterations and selects the best from all trees created. REPTree has been presented to decrease tree structure complexity without reducing classification accuracy. The basic of the pruning of this classifier is used REP with back overfitting. It forms a decision/regression tree by splitting and pruning the regression tree based on the highest information gain ratio value. **Sequential Minimal Optimization (SMO):** This is widely used for training SVMs and was formulated by [PERSON] [31]. SMO is one way to solve a quadratic programming (QP) issue that arises during SVM training. SMO divides the large QP problem into a series of very tiny sub-problems. These small sub-problems are solved analytically, preventing the use of time-consuming numerical QP optimization as an inner loop. It is the fastest for linear SVMs and sparse datasets and can be more than 1000 times faster than the chunking algorithm. The amount of memory needed for SMO is linear in the training dataset size, allowing SMO to handle very large training sets. It scales somewhere between linear and quadratic in the training set size for several test problems. **LogitBoost:** This is a popular boosting variant formulated by [PERSON], [PERSON], and [PERSON] [32]. Researchers can apply it to either binary or multi-class classification, and they considered it an additive tree regression by minimizing logistic loss. LogitBoost has two essential setting factors, invariant property and the density of Hessian matrices, and it is seen as a convex optimization with the following formula: \(f=\sum\limits_{t}\alpha_{t}h_{t}\). Compared with other AdaBoost classifiers, it is appropriate for handling noisy and outlier data. It uses a binomial that changes the loss function linearly. It is one of the popular boosting variants and has three main components: the loss, the function model, and the optimization algorithm. **BayesNet:** This is a broadly used method that works on the basic Bayes theorem; it constructs a Bayesian network [33] by calculating the conditional probability on every node. It is related to the family of the probabilistic graphical model. BayesNet learns in two stages: learning of the network structure followed by the probability table. It is a powerful instrument for data representation and inference under uncertainty conditions. It is a probabilistic graphical algorithm that is used to represent a set of random variables and their conditional dependencies with the assistance of a directed acyclic graph. This graph algorithm is applied to represent knowledge about an uncertain domain. **Radial Basis Function (RBF)**: This is an artificial neural network formulated by [PERSON] and [PERSON] [34]. RBF uses radial basis functions for activation to change along the distance from a location. For functional approximation, it uses time-series prediction, classification, and system control. A multi-layer feedforward neural network, RBF is used to classify data in a non-linear mode and compare input data with training data. The production of the RBF neural network is weighted linear superposition of all basis functions. The frequently used basis function in the RBF model is the Gaussian basis function. **NBTree:** This is a hybrid of naive Bayes and decision trees; it uses both classifiers and has the advantage of univariant splits at each node in the decision tree and the leaves in the naive Bayes algorithm. NBTree is useful when there are many attributes in the classification that are likely to be relevant [35]. It is a highly scalable method for big data and is described as a decision tree with both nodes and branches. NBTree categorizes an example to a leaf and allocates a class label by applying a naive Bayes on that leaf. It represents the learned information in the form of a tree built recursively. NBTree is a popular baseline method for text categorization and with appropriate pre-processing.