ABT-SVDD: : A method for uncertainty handling in domain adaptation using belief function theory

Domain adaptation involves adapting a model trained on one domain to work effectively on another, which can have different statistical properties, such as distributions, correlations, and relationships between features. These heterogeneities can lead to uncertainty, impacting the model’s performance. Despite many studies that have been done on domain adaptation, most have ignored the adverse impact of uncertain and noisy data on adaptation and classification. To address this issue, the proposed method, Adaptive Belief-based Twin Support Vector Data Description (ABT-SVDD), extends the one-class support vector data description (SVDD) to an adaptive twin classifier and integrates it with a belief-based sample weighting approach. Also, it utilizes a combination of Hermite polynomial and Gaussian kernels to enhance the computational power of the linear objective function of the SVDD classifier while improving the generalization capability. The effectiveness of ABT-SVDD has been compared to the state-of-the-art methods on several tasks taken from two benchmark datasets. The experimental results demonstrate that ABT-SVDD significantly improves classification accuracy on various tasks with varying amounts of labeled data in the target domain. Specifically, in normal situations, ABT-SVDD outperforms competing methods by 6.33% to 9.08%, while in noisy situations, it achieves a more significant improvement of 9.87% compared to the competing methods. Besides, the Wilcoxon statistical test demonstrates the superiority of ABT-SVDD over state-of-the-art ones in terms of classification accuracy and computational time.