a b s t r a c t 26 Transfer learning aims to provide a framework to utilize previously-acquired knowledge to solve new 27 but similar problems much more quickly and effectively. In contrast to classical machine learning 28 methods, transfer learning methods exploit the knowledge accumulated from data in auxiliary domains 29 to facilitate predictive modeling consisting of different data patterns in the current domain. To improve 30 the performance of existing transfer learning methods and handle the knowledge transfer process in 31 real-world systems, computational intelligence has recently been applied in transfer learning. This paper 32 systematically examines computational intelligence-based transfer learning techniques and clusters 33 related technique developments into four main categories: (a) neural network-based transfer learning; 34 (b) Bayes-based transfer learning; (c) fuzzy transfer learning, and (d) applications of computational 35 intelligence-based transfer learning. By providing state-of-the-art knowledge, this survey will directly 36 support researchers and practice-based professionals to understand the developments in computational 37 intelligence-based transfer learning research and applications.38

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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 system, especially fuzzy rulebased models, is 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 study presents 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.

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Transfer learning provides an approach to solve target tasks more quickly and effectively by using previouslyacquired knowledge learned from source tasks. Most of transfer learning approaches extract knowledge of source domain in the given feature space. The issue is that single perspective can"t mine the relationship of source domain and target domain fully. To deal with this issue, this paper develops a method using Stacked Denoising Autoencoder (SDA) to extract new feature spaces for source domain and target domain, and define two fuzzy sets to analyse the variation of prediction accuracy of target task in new feature spaces.

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.

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