Fuzzy Multiple-Source Transfer Learning

Abstract

Transfer learning is gaining increasing attention due to its ability to leverage previously acquired knowledge to assist in completing a prediction task in a related domain. Fuzzy transfer learning, which is based on fuzzy systems and particularly fuzzy rule-based models, was developed due to its capacity to deal with uncertainty. However, one issue with fuzzy transfer learning, even in the area of general transfer learning, has not been resolved: how to combine and then use knowledge when multiple-source domains are available. This study presents new methods for merging fuzzy rules from multiple domains for regression tasks. Two different settings are separately explored: homogeneous and heterogeneous space. In homogeneous situations, knowledge from the source domains is merged in the form of fuzzy rules. In heterogeneous situations, knowledge is merged in the form of both data and fuzzy rules. Experiments on both synthetic and real-world datasets provide insights into the scope of applications suitable for the proposed methods and validate their effectiveness through comparisons with other state-of-the-art transfer learning methods. An analysis of parameter sensitivity is also included.