Transfer learning is gaining considerable 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 system (especially fuzzy rule-based models), has been developed because of its capability to deal with the uncertainty in transfer learning. However, two issues with fuzzy transfer learning have not yet been resolved: choosing an appropriate source domain and efficiently selecting labeled data for the target domain. This paper proposes an innovative method based on fuzzy rules that combines an infinite Gaussian mixture model (IGMM) with active learning to enhance the performance and generalizability of the constructed model. An IGMM is used to identify the data structures in the source and target domains providing a promising solution to the domain selection dilemma. Further, we exploit the interactive query strategy in active learning to correct imbalances in the knowledge to improve the generalizability of fuzzy learning models. Through experiments on synthetic datasets, we demonstrate the rationality of employing an IGMM and the effectiveness of applying an active learning technique. Additional experiments on real-world datasets further support the capabilities of the proposed method in practical situations.

中文翻译：

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使用无限高斯混合模型和主动学习的模糊迁移学习


迁移学习因其能够利用先前获得的知识来协助完成相关领域的预测任务而受到广泛关注。模糊迁移学习是基于模糊系统（特别是基于模糊规则的模型）的，由于其能够处理迁移学习中的不确定性而得到发展。然而，模糊迁移学习的两个问题尚未解决：选择合适的源域和为目标域有效选择标记数据。本文提出了一种基于模糊规则的创新方法，将无限高斯混合模型（IGMM）与主动学习相结合，以增强所构建模型的性能和泛化性。 IGMM 用于识别源域和目标域中的数据结构，为域选择困境提供了有希望的解决方案。此外，我们利用主动学习中的交互式查询策略来纠正知识的不平衡，以提高模糊学习模型的泛化能力。通过对合成数据集的实验，我们证明了使用 IGMM 的合理性以及应用主动学习技术的有效性。对真实数据集的额外实验进一步支持了所提出的方法在实际情况下的功能。