.png){kind=icon}
Return
Adaptive Multigranularity Fuzzy Transfer Learning Algorithm
.png){kind=icon}
PRE
AI
DOI：10.1109/TFUZZ.2025.3580443.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Fuzzy transfer learning (FTL) algorithms are capable of leveraging coarse-grained knowledge from source domains to learn related tasks in target domains characterized by inherent uncertainty. However, since the uncertainty in target domains can dynamically change, relying on a single knowledge granularity often proves ineffective for adapting to the learning tasks at hand. To address this challenge, we propose an adaptive multigranularity fuzzy transfer learning algorithm (AMG-FTL) that aims to utilize source knowledge across varying granularity levels. AMG-FTL is distinguished by three main features: First, we establish a knowledge mining mechanism to reconstruct source knowledge at different granularities: fine-grained, medium-grained, and coarse-grained knowledge, allowing the source knowledge to align more closely with the target tasks and bridge the gap between scenarios. Second, we propose an adaptive granularity learning framework aimed at effectively assimilating the diverse levels of source knowledge. This approach is designed to enhance the learning capabilities of the target fuzzy neural network, enabling it to better align with the complexity inherent in the source knowledge while also improving its generalization performance. Additionally, we conduct a theoretical analysis of the proposed AMG-FTL, offering insights into its convergence properties and error bounds. Experimental results across a range of benchmark problems and real-world applications indicate that AMG-FTL significantly outperforms several leading algorithms in the field.
Keywords:
Adaptive knowledge granularity
fuzzy transfer learning (FTL) algorithm
generalization performance
knowledge over-fitting
knowledge under-fitting
Journal
IF:
11.9
Papers:
5.0K
Citations:
2.9W
Organization
View more organizations
Cited Papers
No cited papers available