Abstract Most of the classification approaches assume that the sample distribution among classes is balanced. Still, such an assumption leads to biased performance over the majority class. This paper proposes an enhanced automatic twin support vector machine – (EATWSVM) to deal with imbalanced data, which incorporates a kernel representation within a TWSVM-based optimization. To learn the kernel function, we impose a Gaussian similarity, ruled by a Mahalanobis distance, and couple a centered kernel alignment-based approach to improving the data separability. Besides, we suggest a suitable range to fix the regularization parameters concerning both the dataset’ imbalance ratio and overlap. Lastly, we adopt One-vs-One and One-vs-Rest frameworks to extend our EATWSVM formulation for multi-class tasks. Obtained results on synthetic and real-world datasets show that our approach outperforms state-of-the-art methods concerning classification performance and training time.

中文翻译：

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用于不平衡数据分类的增强型自动双支持向量机

摘要 大多数分类方法假设类之间的样本分布是平衡的。尽管如此，这种假设会导致对多数类的性能有偏见。本文提出了一种增强型自动双支持向量机（EATWSVM）来处理不平衡数据，它在基于 TWSVM 的优化中结合了内核表示。为了学习核函数，我们强加了由马哈拉诺比斯距离支配的高斯相似度，并结合了基于中心核对齐的方法来提高数据可分离性。此外，我们建议一个合适的范围来修复关于数据集不平衡率和重叠的正则化参数。最后，我们采用 One-vs-One 和 One-vs-Rest 框架来扩展我们的多类任务的 EATWSVM 公式。