Enhancing Imbalanced Classification with Support Vector Machines via Evolutionary Oversampling Algorithms

Support Vector Machines (SVMs), as well-known algorithms, have been successfully applied to classification problems. However, when dealing with imbalanced data, the classification performance of SVMs could be significantly compromised. One approach to tackle the class imbalance is oversampling the minority class, exemplified by methods like SMOTE and its variants. These methods generate new samples by interpolation between existing ones and determine the weights based on the ratio of samples from different classes, leading to inaccurate weight assignment, limited generation scope, and indiscriminate sample generation. To address these limitations, we propose novel evolutionary oversampling algorithms based on Support Vector Machine (SVM) and Evolutionary Algorithms (EAs) called SEOA. SEOA leverages the inherent capability of SVM to identify the samples that have a critical influence on the decision boundary and assign them appropriate weights, thereby eliminating the reliance on human experience. Furthermore, SEOA utilizes a novel approach for sample generation and emphasizes the significance of margin for classification, introducing a mechanism that employs margin as the metric to evaluate the quality of generated samples. To assess the performance of SEOA, we conducted a comprehensive comparison against various oversampling methods across 19 real-world datasets. The results underscore SEOA's superiority, showcasing its distinct strengths in addressing the challenges posed by imbalanced classification.