Partial feedback online transfer learning with multi-source domains
Partial feedback online transfer learning with multi-source domains
复制标题
DOI：
10.1016/j.inffus.2022.07.025
复制
发表时间：
2022-08
期刊：
影响因子：
--
通讯作者：
Zhongfeng Kang;Mads Nielsen;Bo Yang;Mostafa Mehdipour-Ghazi
中科院分区：
文献类型：
--
作者：
Zhongfeng Kang;Mads Nielsen;Bo Yang;Mostafa Mehdipour-Ghazi
在没有静态数据集的情况下，在线机器学习是一种有效的基于观察的学习方法。然而，这在实际应用程序中可能具有挑战性，特别是在多类分类任务中缺少标签的情况下。虽然可以应用部分反馈来解决这个问题，但它会使学习过程变慢，并限制分类性能，因为当实例被错误分类时，会丢失正确的标签信息。为了解决在线学习中目标领域知识缺乏的问题，迁移学习可以将一个或多个源领域的知识传递到目标领域。为此，我们提出了一种多源域部分反馈在线迁移学习算法（PFMSD），将从多源域学习到的知识转移到目标域，并通过在存在错误预测时探索正确的标签来提高学习性能。推导了该算法的错误边界，并使用几个广泛使用的基准数据集进行了大量实验。实验结果表明，该算法优于现有的部分反馈算法。
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.