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Heterogeneous transfer learning: recent developments, applications, and challenges

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Abstract

Transfer learning (TL) has emerged as a promising area of research in machine learning (ML) due to its ability to enhance learning efficiency and accuracy by leveraging knowledge from related domains. However, traditional TL is limited in its applicability to real-world scenarios where the assumption of identical feature spaces and distributions between source and target domains is untenable. To address this limitation, Heterogeneous Transfer Learning (HeTL) has emerged as an important research direction that enables knowledge transfer between domains with heterogeneous feature spaces and distributions. Motivated by the growing interest and significance of HeTL, this survey paper comprehensively reviews recent HeTL developments, beginning with mathematical TL definitions and a taxonomy of TL categories. It delves into HeTL, explaining its classification and research status, and highlights symmetric and asymmetric HeTL advancements. Next, we explored the applications of HeTL in various disciplines, such as image and text classification, activity recognition, and cross-project defect prediction, emphasizing HeTL’s advantages over Traditional TL. Furthermore, we also discuss the challenges in HeTL, such as heterogeneity, transferability, negative learning, interpretability, and explainability. Finally, we conclude with a discussion on HeTL directions for future research.

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Data sharing is not applicable to this article as no new data were generated or analyzed during the current study.

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Acknowledgements

This work was supported by National Natural Science Foundation of China (NSFC) 62272172, Guangdong Basic and Applied Basic Research Foundation 2023A1515012920. Also, the authors would like to thanks Prince Sultan University for their support.

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Authors and Affiliations

  1. School of Software Engineering, South China University of Technology, Guangzhou, China

    Siraj Khan

  2. Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ), Shenzhen, China

    Pengshuai Yin

  3. Guangzhou Institute of Science and Technology, Guangzhou, China

    Yuxin Guo

  4. EIAS Data Science Lab, College of Computer and Information Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia

    Muhammad Asim & Ahmed A. Abd El-Latif

  5. School of Computer Science and Technology, Guangdong University of Technology, Guangzhou 510006, China

    Muhammad Asim

  6. Department of Mathematics and Computer Science, Faculty of Science, Menoufia University, Shebin El-Koom 32511, Egypt

    Ahmed A. Abd El-Latif

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Khan, S., Yin, P., Guo, Y. et al. Heterogeneous transfer learning: recent developments, applications, and challenges. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-024-18352-3

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