Kernel-Free Universum Quadratic Surface Twin Support Vector Machines for Imbalanced Data
This addresses the problem of biased models in imbalanced classification for machine learning practitioners, though it appears incremental as it builds on existing twin support vector machine frameworks.
The paper tackles binary classification with imbalanced classes by introducing a kernel-free Universum quadratic surface twin support vector machine approach, which uses quadratic surfaces and Universum points to support the minority class. It demonstrates superior performance on benchmark datasets compared to conventional methods.
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.