# Ad-RuLer: A Novel Rule-Driven Data Synthesis Technique for Imbalanced Classification
## 2 Related Work  
In many application scenarios, the goal of imbalance learning is to optimize the classification performance of minority class samples and the overall classification performance. Research to address this class imbalance problem can be summarized into three methods: data-based [15; 16; 17], model-based [18; 19; 20], and hybrid approaches [21]. Model-based methods focus on algorithmic improvements to the imbalance problem, with cost-sensitivelearning being a prime example. This strategy assigns different weights to the samples within a dataset, particularly in situations of class imbalance where minority class samples are prone to misclassification. Therefore, by constructing an appropriate cost-sensitive matrix to adjust the misclassification cost of samples from different classes, it provides an alternative way to balance the dataset. On the other hand, data-based methods primarily construct a new balanced dataset by altering the original data distribution. These methods can be further categorized into oversampling, undersampling, and hybrid sampling. Oversampling aims to increase the minority class samples in the original dataset by creating additional new samples. Undersampling, however, reduces the sample size of the original dataset by eliminating redundant majority class samples. Hybrid sampling combines the strategies of undersampling and oversampling to achieve a balanced treatment of the original dataset. In the realm of undersampling techniques, the random undersampling (RUS) algorithm emerges as a foundational approach. It aims to achieve class imbalance by randomly discarding a portion of the majority class samples. However, the stochastic nature of RUS can inadvertently omit crucial characteristics embedded within the samples. Tomek-link pairs were introduced as an evolution to address this limitation, as highlighted by [22]. This method advocates for the removal of majority class samples present within Tomek-links pairs, building on the premise that such samples might represent noise or belong to boundary regions. Yet, the presence of Tomek-link pairs in datasets can sometimes limit the improvements offered by this technique in terms of classification accuracy. Branching into more advanced strategies, [PERSON] and [PERSON] [23] proposed an undersampling technique rooted in the principles of clustering and inter-sample distances, all while factoring in the disparity in class distribution. With clustering as its cornerstone, it guides the removal of select majority class samples to balance the dataset. In a parallel vein, [PERSON] et al. [24] employed clustering to identify majority class samples, subsequently culling their quantity by opting for cluster centroids. Nevertheless, it is imperative to note that while undersampling approaches are adept at countering class imbalance, the removal of majority class samples might inadvertently lead to the loss of critical information from the dataset. This issue becomes more pronounced in scenarios with already limited sample sizes. Oversampling methods, as the mainstream solution for imbalanced classification, are versatile and compatible with various machine learning classifiers. [PERSON] et al. [25] introduced the Synthetic Minority Oversampling Technique (SMOTE). SMOTE synthesizes minority class samples via interpolation between a data point and its neighbors, achieving sample balance. It overcomes the issue of duplicating sample data in random oversampling (ROS) algorithms, enabling the creation of diverse new samples based on linear interpolation. However, SMOTE's random selection and generation of sample points do not fully consider the internal data structure, potentially leading to misclassification. To address this, [PERSON] et al. [26] proposed the Borderline-SMOTE algorithm, a boundary-based method that combines SMOTE with boundary information. To identify boundary samples more accurately, [PERSON] et al. [27] introduced an optimized oversampling method, the Majority Weighted Minority Over-sampling Technique (MWMOTE).