Partial feedback online transfer learning with multi-source domains
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Partial feedback online transfer learning with multi-source domains. / Kang, Zhongfeng; Nielsen, Mads; Yang, Bo; Ghazi, Mostafa Mehdipour.
In: Information Fusion, Vol. 89, 2023, p. 29-40.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - Partial feedback online transfer learning with multi-source domains
AU - Kang, Zhongfeng
AU - Nielsen, Mads
AU - Yang, Bo
AU - Ghazi, Mostafa Mehdipour
PY - 2023
Y1 - 2023
N2 - Online machine learning is an effective way for observation-based learning when a static dataset is not available. However, it can be challenging in real-world applications, especially when there are missing labels in multi-class classification tasks. Although partial feedback can be applied to tackle the problem, it can make the learning process slow and limit the classification performance as the correct label information is missing when the instance is misclassified. To cope with the lack of the target domain knowledge in online learning, transfer learning can be applied to convey knowledge from one or multiple source domains to the target domain. To this end, we propose a partial feedback online transfer learning algorithm with multiple source domains (PFMSD) to transfer the knowledge learned from multi-source domains to the target domain and enhance the learning performance by exploring the correct label when there is an erroneous prediction. A mistake bound is derived for the proposed algorithm, and extensive experiments are conducted using several wildly-used benchmark datasets. The obtained results in all experiments show the superiority of the proposed algorithm over the state-of-the-art partial feedback algorithms.
AB - Online machine learning is an effective way for observation-based learning when a static dataset is not available. However, it can be challenging in real-world applications, especially when there are missing labels in multi-class classification tasks. Although partial feedback can be applied to tackle the problem, it can make the learning process slow and limit the classification performance as the correct label information is missing when the instance is misclassified. To cope with the lack of the target domain knowledge in online learning, transfer learning can be applied to convey knowledge from one or multiple source domains to the target domain. To this end, we propose a partial feedback online transfer learning algorithm with multiple source domains (PFMSD) to transfer the knowledge learned from multi-source domains to the target domain and enhance the learning performance by exploring the correct label when there is an erroneous prediction. A mistake bound is derived for the proposed algorithm, and extensive experiments are conducted using several wildly-used benchmark datasets. The obtained results in all experiments show the superiority of the proposed algorithm over the state-of-the-art partial feedback algorithms.
KW - Online learning
KW - Transfer learning
KW - Partial feedback
KW - Multi-class classification
KW - Multi-source domains information fusion
KW - KERNEL
U2 - 10.1016/j.inffus.2022.07.025
DO - 10.1016/j.inffus.2022.07.025
M3 - Journal article
VL - 89
SP - 29
EP - 40
JO - Information Fusion
JF - Information Fusion
SN - 1566-2535
ER -
ID: 330837472