New Multi-Source Distributed Transfer Learning Framework
Linqing Huang, Jing Zhu, Yumei Hu, Shilin Wang, Gongshen Liu, Jinfu Fan · 2025
In pattern recognition, where the labeled data is scarce, transfer learning (also called domain adaptation in some cases) methods frequently come into play to transfer knowledge from the source domains to bolster the construction of classification models within the target domain. The judicious fusion of information from multiple source domains typically enhances classification precision. In light of this, we introduce a new Multi-source Distributed Transfer Learning (MDTL) framework designed to adeptly integrate complementary information across various source domains through the application of belief functions. In this approach, the distributions of each source and target domain are aligned independently. Subsequently, the resultant soft classification outcomes, facilitated by different source domains, are amalgamated using belief functions. This integration incorporates novel weighting factors that consider both distribution discrepancies and classifier effectiveness. The effectiveness of MDTL was assessed against a range of related methods, and the experimental findings confirm that it markedly improves classification accuracy in the target domain.