IEEE Trans Cybern. 2022 Aug;52(8):8101-8113. doi: 10.1109/TCYB.2021.3052536. Epub 2022 Jul 19.
In pattern classification, we may have a few labeled data points in the target domain, but a number of labeled samples are available in another related domain (called the source domain). Transfer learning can solve such classification problems via the knowledge transfer from source to target domains. The source and target domains can be represented by heterogeneous features. There may exist uncertainty in domain transformation, and such uncertainty is not good for classification. The effective management of uncertainty is important for improving classification accuracy. So, a new belief-based bidirectional transfer classification (BDTC) method is proposed. In BDTC, the intraclass transformation matrix is estimated at first for mapping the patterns from source to target domains, and this matrix can be learned using the labeled patterns of the same class represented by heterogeneous domains (features). The labeled patterns in the source domain are transferred to the target domain by the corresponding transformation matrix. Then, we learn a classifier using all the labeled patterns in the target domain to classify the objects. In order to take full advantage of the complementary knowledge of different domains, we transfer the query patterns from target to source domains using the K-NN technique and do the classification task in the source domain. Thus, two pieces of classification results can be obtained for each query pattern in the source and target domains, but the classification results may have different reliabilities/weights. A weighted combination rule is developed to combine the two classification results based on the belief functions theory, which is an expert at dealing with uncertain information. We can efficiently reduce the uncertainty of transfer classification via the combination strategy. Experiments on some domain adaptation benchmarks show that our method can effectively improve classification accuracy compared with other related methods.
在模式分类中，我们可能在目标域中只有少数标记数据点，但在另一个相关领域（称为源域）中有大量标记样本可用。迁移学习可以通过从源域到目标域的知识转移来解决此类分类问题。源域和目标域可以用异质特征表示。域转换中可能存在不确定性，这种不确定性不利于分类。有效管理不确定性对于提高分类准确性非常重要。因此，提出了一种新的基于置信度的双向迁移分类（BDTC）方法。在 BDTC 中，首先估计类内变换矩阵以将模式从源域映射到目标域，并且可以使用由异质域（特征）表示的同一类的标记模式来学习该矩阵。源域中的标记模式通过相应的变换矩阵转移到目标域。然后，我们使用目标域中的所有标记模式学习分类器来对目标进行分类。为了充分利用不同域的互补知识，我们使用 K-NN 技术将查询模式从目标域转移到源域，并在源域中执行分类任务。因此，对于源域和目标域中的每个查询模式，都可以得到两个分类结果，但分类结果可能具有不同的可靠性/权重。我们根据置信函数理论开发了一种加权组合规则来组合两个分类结果，置信函数理论是处理不确定信息的专家。通过组合策略，我们可以有效地降低迁移分类的不确定性。在一些域自适应基准上的实验表明，与其他相关方法相比，我们的方法可以有效地提高分类准确性。
