Multi-Source Transfer Learning Based on Fuzzy Rules for Improving Image Classification Accuracy

4 62 50One notable aspect of fuzzy transfer learning, and transfer learning in general, is the unresolved question of effectively combining and utilizing knowledge when multiple source domains are available.This issue is particularly pertinent in scenarios involving diverse datasets from various sources.Consequently, the present study introduces a novel approach to multi-source transfer learning anchored in fuzzy rules.By integrating fuzzy logic, the proposed method aims to provide a robust solution to the challenges posed by knowledge transfer in scenarios with multiple source domains.This research contributes to advancing transfer learning methodologies, offering a nuanced perspective on handling uncertainty in multi-source environments by applying fuzzy rule-based techniques.In conclusion, the significance of transfer learning in image classification tasks is underscored by the inherent challenges of acquiring labeled training data.The conventional focus on single-source to singletarget domain transfer has limitations, prompting a shift towards addressing the more realistic and challenging scenarios of multi-source domain adaptation.This study introduces a pioneering approach to multi-source transfer learning, utilizing fuzzy rule-based techniques to effectively navigate the complexities introduced by knowledge transfer from multiple sources.Through this contribution, the research aims to propel advancements in transfer learning methodologies and foster a more comprehensive understanding of handling uncertainty in multi-source environments.