# Hybrid Oversampling and Undersampling Method (HOUM) via Safe-Level SMOTE and Support Vector Machine
## 2 Literature Review
Another study proposed an undersampling method based on the KNN algorithm; samples are removed according to the number of basic neighbors of each class to balance the data [12]. The proposed algorithm was tested on 33 datasets and compared with six methods. Compared with other methods, the results confirmed the validity of the KNN undersampling method. Recent detailed survey work [13] reviews deep long-tailed learning, where class distributions are imbalanced, with a few classes having many samples and most having very few (a \"long tail\"). It categorizes methods into three key areas: class re-balancing, information augmentation, and module improvement, while introducing relative accuracy to evaluate how effectively these methods address class imbalance. Although many of the methods discussed in this survey were initially designed for visual applications, they can be adapted to traditional machine learning problems. CReST (A Class-Rebalancing Self-Training Framework for Imbalanced Semi-Supervised Learning) [14] introduces a self-training strategy that addresses class imbalance in semi-supervised learning by leveraging accurate pseudo-labels, especially for underrepresented classes, to progressively retrain models. CReST+ further enhances this approach with progressive distribution alignment, and both methods outperform traditional semi-supervised learning and rebalancing techniques across different datasets. The FASA (Feature Augmentation and Sampling Adaptation) [15] method creates synthetic features for underrepresented classes using a Gaussian prior and adjusts sampling rates according to the model's classification loss. This adaptive approach prioritizes the augmentation of minority classes, enhancing performance on imbalanced datasets. To gain a more thorough understanding of long-tailed visual recognition, readers may explore additional studies on the topic [16; 17]. The aim of hybrid methods, through the use of oversampling and undersampling, is to conquer the class imbalance problem and achieve better performance metrics [3]. In the work of [PERSON] et al. [18], to handle the class imbalance problem, instances of the majority class were grouped into subclasses via an unsupervised learning algorithm. The proposed class decomposition technique (CDSMOTE) not only reduced the dominance of the majority class but also obstructed information loss. In the study, the oversampling method was applied after the undersampling procedure. [PERSON] et al. [19] proposed two methods, namely, SMOTEENN and SMOTETomek, which work as a combination of over- and undersampling methods. SMOTEENN is a two-step process. First, SMOTE is applied to oversample the minority class. Then, ENN is applied to the resulting dataset to remove instances that are considered noisy or potentially mislabeled. SMOTETomek involves applying SMOTE to oversample the minority class and then using Tomek links to clean the dataset. After SMOTE is applied, Tomek links are identified, and instances involved in these links are removed. RHSBoost [20] uses random subsampling and random oversampling methods with a reinforcement scheme in the developed batch classification method. Based on the experimental results, RHSBoost is a successful classification model for imbalanced data. The RUSBoost algorithm, which is presented by [PERSON] et al. [21] for learning from skewed training data, combines boosting and random undersampling. In another hybrid method, RusAda [22], based on RUSBoost, the resampled training dataset is used to build the iteration model. The boosting procedure is used in AdaBoost to improve the algorithm's performance. In another hybrid approach [23], to solve the classification problem of two class imbalanced datasets, firstly, the number of minority samples was increased with SMOTE, and the majority-class samples were reduced with the OSS (one-sided selection) method, and an SVM was used as a classifier. [PERSON] et al. [24] compiled data mining and machine learning algorithms used in the textile sector.