Domain Selection of Transfer Learning in Fuzzy Prediction Models

Abstract

Transfer learning has emerged as a solution for the cases where little or no labeled data are available in the training process. It leverages the previously acquired knowledge (a source domain with a large amount of labeled data) to facilitate solving the current tasks (a target domain with little labeled data). Many transfer learning methods have been proposed, and especially fuzzy transfer learning method, which is based on fuzzy systems, has been developed because of its capability to deal with the uncertainty in transfer learning. However, there is one issue with fuzzy transfer learning that has not yet been resolved: the domain selection problem, which is heavily depended on the knowledge transfer method and the applied prediction model. In this work, we explore the domain selection problem in TakagiSugeno fuzzy model when multiple source domains are accessible, and define the similarity between the source and target domains to provide guidance for the domain selection. The experiments on synthetic datasets are designed to simulate the situations of multiple sources in transfer learning, and demonstrate the rationality of the proposed similarity in selecting the source domain for the target domain. Further, the real-world datasets are used to validate the proposed domain adaptation method, and verify its capability in solving practical situations.