# Hybrid Oversampling and Undersampling Method (HOUM) via Safe-Level SMOTE and Support Vector Machine
## 2 Literature Review  
The existing literature on imbalanced data can be examined within certain subgroups, such as pre-processing, algorithmic, and hybrid paradigms, as proposed by [PERSON] et al. [3]. However, in this section, considering the main frame of the algorithm, the studies on oversampling, undersampling, and hybrid methodologies and studies focusing on DSSs via machine learning in textile production are reviewed. Random oversampling can be achieved by randomly selecting and increasing minority data and adding them to the original dataset. This method is simple, but exact copies may increase the possibility of overfitting for the datasets that require high oversampling [4]. The most used oversampling method is the SMOTE (Synthetic Minority Oversampling Technique) approach [5]. Unlike random sampling, this method creates synthetic data, analyzing existing minority data. SMOTE cannot reflect the distribution of the original samples in the new artificial samples. Therefore, when using SMOTE-based oversampling methods, there may be errors in the distribution of samples, which may affect the accuracy of the classifier. This will increase the probability and cause the misclassification of samples [6]. [PERSON] et al. [2] proposed a method called SLS (safe-level SMOTE). The safe level is determined using the nearest neighbor minority samples, and the minority data with the same weight value in the safe-level region are carefully sampled along the line. The authors proved in the study that this method obtained better results than SMOTE. In another study, ESMOTE was proposed as a remedy to the noise problem of SMOTE. In the study, the novel interpolation technique was developed in the production phase of samples, and the selection of beneficial samples was conducted using instance selection based on evolutionary computation [7]. [PERSON] et al. [8] conducted an oversampling study to address the multiclass imbalance problem and the analysis of class characteristics. In the study, subsets of significant samples could be found in each class and considered by oversampling independently for each. This methodology identifies four different types of samples in multiclass datasets, namely, safe, borderline, rare, and outliers. In both the SIMO and W-SIMO methods, minority examples that are close to the decision boundary, as determined using the SVM, are oversampled [1]. The aim is to reproduce only informative minority data to avoid over-learning while increasing minority data. In the W-SIMO approach, informative minority samples that are misclassified are subjected to a greater degree of oversampling compared to those informative minority samples that are correctly classified. The results of the study were evaluated according to the G-mean criterion, and the method was demonstrated to outperform the commonly used methods such as SMOTE and random oversampling. In another study by [PERSON] et al. [9], a technique based on relative and absolute densities was developed and compared with well-known oversampling methods to resolve imbalances within and between classes. The random undersampling method is a non-heuristic method that randomly removes data from the majority class until the minority and majority classes reach a reasonable size. This data reduction process may also lead to useful information being discarded during classification. The methods aiming for better data distribution or focusing on data overlap usually provide superior classification performance. In the paper of [PERSON] and [PERSON] [10], the researchers proposed an undersampling technique in binary datasets via the removal of potentially overlapped data points. The method's performance in terms of sensitivity is verified by experiments. [PERSON] et al. [11] addressed the undersampling approach using OPTICS, one of the visualization clustering techniques, to solve the class imbalance problem. The OPTICS clustering technique was incorporated to undersample the majority class.