# Ad-RuLer: A Novel Rule-Driven Data Synthesis Technique for Imbalanced Classification
## 2 Related Work
This innovative algorithm takes into account the neighboring information of both majority and minority class samples, bolstering boundary identification via reciprocal validation of neighbor conditions. Subsequently, the identified boundary samples of minority classes are clustered, and interpolation is performed within each cluster to generate new minority class samples. Expanding on this concept, [PERSON] et al. [28] proposed the Certainty Guided Minority OverSampling (CGMOS) algorithm, which places a particular emphasis on minority class samples. The algorithm determines the weight of each minority class sample selected based on relative certainty. These weights then inform the probability of selecting a seed sample for new sample synthesis. The next step involves generating new minority class sample data via interpolation, guided using the calculated probabilities. [PERSON] et al. [29] suggested an oversampling method combining k-means clustering with SMOTE to perform space division, effectively avoiding noise generation and addressing both inter-classand intra-class imbalances. [PERSON] et al. [30] proposed the ADASYN algorithm, an adaptive synthetic sampling method that dynamically determines the number of synthetic samples based on sample distribution, synthesizing more minority class samples in hard-to-classify categories to achieve data balance. In recent years, some new approaches to imbalanced learning have emerged in the literature [31; 32]. A novel hybrid sampling method was proposed by [PERSON] et al. [33]. This method employs k-means clustering to categorize samples, followed by calculating the density of each sub-cluster. By selectively oversampling within less populated minority class sub-clusters and simultaneously undersampling from denser majority class segments, this method adeptly enhances the representation of minority samples while trimming superfluous majority class data, optimizing the overall classification results. [PERSON] and [PERSON] [34] presented a method that integrates ROS, k-means clustering, and a support vector machine hybrid model. This approach first oversamples the minority class samples randomly and then uses k-means clustering to identify the majority class samples closer to the boundary. The advantage of this strategy is that it decreases the risk of accidentally deleting important samples from the majority class, which enhances the efficacy of sample selection. Deep learning has increasingly become a focal point in research on imbalanced data classification in the past few years. Inspired by the random covering problem, [PERSON] et al. [35] proposed the data sampling process by associating each sample with a small neighboring area, dynamically guiding the sampling through the computation of an effective sample size. [PERSON] et al. [36] utilized generative models to create new samples for minority classes using convex combinations of existing examples, while [PERSON] et al. [37] sought to synthesize minority class samples by introducing learnable noise to majority class samples. Based on contrastive learning, some research aims to enhance the precision of models in classifying minority classes by improving the capability of representation learning [38; 39]. In the realm of graph deep learning for imbalanced classification, [PERSON] et al. [40] proposed a Dual-Regularized Graph Convolutional Network (DRGCN), which employs conditional adversarial training and distribution alignment training to differentiate nodes of various classes and balances the learning between majority and minority categories. The GraphSMOTE framework, introduced by [PERSON] et al. [41], encodes the similarity between nodes within an embedding space and synthesizes new nodes and edges to create a balanced graph for node classification. [PERSON] et al.