# Ad-RuLer: A Novel Rule-Driven Data Synthesis Technique for Imbalanced Classification
## 1 Introduction  
In recent years, the explosive growth of big data, amplified by significant strides in artificial intelligence, has led industries, from finance and healthcare to online platforms, to produce unprecedented amounts of data. As we navigate this data-rich environment, the challenge of imbalanced class distribution has emerged prominently. This issue, characterized by certain classes being notably underrepresented compared to others, negatively impacts the accuracy of predictive models. Traditional machine learning approaches were conceived, assuming a roughly balanced data distribution. However, real-world datasets frequently deviate from this assumption. In some domains, achieving precise classification of minority classes is critical, given the steep repercussions associated with misclassification [1; 2]. For instance, in the healthcare sector, the number of patients diagnosed with specific diseases (positive samples) is often significantly lower than those without such conditions (negative samples). Consequently, standard classifiers, which tend to favor the majority class, are prone to misclassification. Such inaccuracies may lead to critical delays in medical treatments, jeopardizing patient health. Similarly, in finance, only a small percentage of customers might have poor credit ratings, but misclassifying them can expose institutions to significant risks. Beyond the algorithmic challenges, these imbalances have tangible adverse effects in areas like financial fraud detection, intrusion detection, and product quality assessments. To address class imbalances, researchers have developed data resampling techniques, including oversampling [3; 4; 5], undersampling [6; 7; 8], and hybrid sampling [9; 10; 11]. In recent years, the oversampling technique has attracted widespread attention within the academiccommunity due to its ability to amplify underrepresented classes, enhancing the classifier's sensitivity towards minority instances. This technique proves particularly advantageous in scenarios where each instance of the minority class is crucial, such as rare disease diagnosis or financial fraud detection. Current oversampling methods primarily rely on interpolation techniques, aiming to achieve class balance by synthesizing samples for the minority class. However, these approaches also have some limitations, including over-constraint, low-efficiency expansion, and over-generalization [12]. Despite these challenges, there is still a relative dearth of research on alternative oversampling strategies, with rule-based oversampling algorithms, in particular, being less explored. Recent studies have demonstrated the advantages inherent in rule-based oversampling methods, which are characterized by their robust adaptability, consistent performance, and superior interpretability [13]. Such attributes indicate that rule-based strategies have substantial potential for addressing issues of data imbalance. An in-depth exploration of these methods stands to not only enrich the existing research of oversampling techniques, but also potentially offer new insights into fields such as imbalanced data classification and predictive modeling. In this study, we present Ad-RuLer, an innovative adaptive rule-based oversampling approach. Distinct from the prevalent interpolation-based data resampling methods, Ad-RuLer employs an iterative comparison mechanism to rapidly extract intrinsic rules from datasets, which are then used to synthesize new instances for the minority class. This algorithm builds on the principles of RuLer--an algorithm originally designed for detecting novel sound patterns in the field of live-coding performance art [14]. In this study, we apply RuLer for the first time to the problem of data oversampling, addressing the challenge of imbalanced data classification. The Ad-RuLer has been benchmarked against several mainstream data resampling techniques using ten diverse imbalanced datasets. Our research findings highlight its superior performance and notable application potential.