## 4 Non-Ensemble-Based Methods  
Different research papers have empirically and experimentally reported that the classification of a balanced dataset is somewhat elementary and comfortable to perform, but it becomes difficult when the data are not balanced [57]. The standard machine learning \begin{table}
\begin{tabular}{l l} \hline \hline
**Symbol** & **Description** \\ \hline \(D_{j}\) & Sample of class \(j\) \\ \(p_{j}^{(C_{i})}\) & Number of accurate predications of the classifiers \(i\) \\ \(\chi A\) & Characteristic function which takes into account the prediction \(j\in A\) \\ \(c_{rs}\) & Coefficients \\ \(w_{rs}\) & Weak learners \\ \(z_{b}^{l}\) & \(b\)-th observation of the \(l\)-th bootstrap sample \\ \(L\) & Bootstrap samples \\ \(C\) & Regularization parameter \\ \(\lambda\) & Kernel width parameter \\ \(n_{estimator}\) & Number of trees \\ \(leaf_{size}\) & \(leaf_{size}\) parameters \\ \(K\) & Neighbor at \(k\) distance \\ \(max_{depth}\) & Last leaf node of the tree \\ \(y_{i}\) and \(\hat{y}_{i}\) & Actual value and predicted value of observation \(i\) \\ \(C_{i}\) & Class \(i\)th sample \\ \(C_{j}\) & Class \(j\)th sample \\ \(r_{fi}\) & Discriminative ratio \\ \(d(\overrightarrow{a},\overrightarrow{b})\) & Distance of two vector (two samples of the classes) \\ \(d_{i}\) & Average distance \\ \(k_{1}\) & Nearest neighbors \\ \(N_{ei}\) & Sample in the target class \\ \(\text{MC}_{Count}\) & Samples in the majority class \\ \(Col\) & Column in the dataset \\ \(Loc\) & Location of the sample \\ \(S\) & \(S\) Set of synthetic samples \\ \(T\) & \(T\) Subset of \(D\) (multiclass dataset) \\ \(y_{new}\) & New minority \\ \(f_{i}\) & Feature listed in dataset \\ \(Learning_{rate}\) and \(number-of-tree\) & The boosting parameters directly related to the underlying boosting algorithm \\ \(N\) & Total number of observations \\ \(E(.)\) & Fitting error \\ \(e(.,.)\) & Loss/error function \\ \(S_{l}\) & Model that best fits the training data \\ \(p_{cj}p_{ck}\) & Represent the respective samples in classes \(c_{j}\) and \(c_{k}\) \\ \(\mu^{f_{i}}\) & Mean of the \(f_{i}\) values across all classes \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Symbol and their description. algorithm assumes a balanced distribution of classes in a subset of the dataset used for classification; however, the distribution of instances in classes is not uniform in many real-life situations [58]. Traditional classifiers, initially designed for the classification of balanced datasets, show a significant performance for the underlying problem of having a balanced dataset. On the other hand, real-world data are messy with an unequal distribution of samples, where the traditional classifiers fail to properly identify the relevant target class. Figure 1 shows the imbalanced distribution of samples. This imbalance distribution of instances causes biases toward the majority group, thus creating difficulty for the standard learning algorithm to correctly predict an unseen sample. The ignorance of the minority class will lead to building a poor model, as sometimes, this leased-focused class carries important information, thus providing an impractical classifier for our proposed use case. Many standard classification algorithms, namely the SVM, ANN, DT, NB classifier, and KNN, which are designed based on balanced training datasets, become less effective due to skewed distribution in imbalance classes [59]. Although some of them can use for a small dataset as imbalanced data, if we apply the traditional algorithms to multi-class and multi-label problems with an imbalanced dataset, a good performance in terms of accuracy [60] is not necessarily achieved, as standard algorithms are built with the assumption that the distribution is balanced. Therefore, when presented with large imbalanced datasets, these algorithms fail to properly represent the distributive characteristics of data [1]. Classification problems in many real-world applications and scenarios involve multiple classes both in a balanced and in an imbalanced dataset.