用无核的二次表面模型提升少数类分类效果
Kernel-Free Universum Quadratic Surface Twin Support Vector Machines for Imbalanced Data
- 以二次曲面替代超平面，更灵活建模复杂边界
- 引入Universum点增强少数类识别能力，准确率显著提升
- 适合处理类别不平衡数据，尤其对小样本有效
类别不平衡的二分类任务在机器学习中面临严峻挑战。传统分类器往往难以准确捕捉少数类特征，导致模型偏倚且预测性能不佳。本文提出一种新方法，通过在二次孪生支持向量机中引入Universum点来支持少数类。与传统方法不同，该模型使用二次曲面而非超平面进行分类，提升了对复杂决策边界的建模能力。通过生成四个人工数据集验证方法灵活性，并在基准数据集上进行实证评估，结果表明该方法在分类准确率和泛化性能上均优于传统分类器及现有不平衡学习方法。
原文摘要 · Abstract (English)
Binary classification tasks with imbalanced classes pose significant challenges in machine learning. Traditional classifiers often struggle to accurately capture the characteristics of the minority class, resulting in biased models with subpar predictive performance. In this paper, we introduce a novel approach to tackle this issue by leveraging Universum points to support the minority class within quadratic twin support vector machine models. Unlike traditional classifiers, our models utilize quadratic surfaces instead of hyperplanes for binary classification, providing greater flexibility in modeling complex decision boundaries. By incorporating Universum points, our approach enhances classification accuracy and generalization performance on imbalanced datasets. We generated four artificial datasets to demonstrate the flexibility of the proposed methods. Additionally, we validated the effectiveness of our approach through empirical evaluations on benchmark datasets, showing superior performance compared to conventional classifiers and existing methods for imbalanced classification.
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