In applications of domain adaptation, there may exist multiple source domains, which can provide more or less complementary knowledge for pattern classification in the target domain. In order to improve the classification accuracy, a decision-level combination method is proposed for the multisource domain adaptation based on evidential reasoning. The classification results obtained from different source domains usually have different reliabilities/weights, which are calculated according to domain consistency. Therefore, the multiple classification results are discounted by the corresponding weights under belief functions framework, and then, Dempster's rule is employed to combine these discounted results. In order to reduce errors, a neighborhood-based cautious decision-making rule is developed to make the class decision depending on the combination result. The object is assigned to a singleton class if its neighborhoods can be (almost) correctly classified. Otherwise, it is cautiously committed to the disjunction of several possible classes. By doing this, we can well characterize the partial imprecision of classification and reduce the error risk as well. A unified utility value is defined here to reflect the benefit of such classification. This cautious decision-making rule can achieve the maximum unified utility value because partial imprecision is considered better than an error. Several real data sets are used to test the performance of the proposed method, and the experimental results show that our new method can efficiently improve the classification accuracy with respect to other related combination methods.

中文翻译：

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基于证据推理的可转移分类与多源域适应的结合。

在域自适应应用中，可能存在多个源域，这些源域可以为目标域中的模式分类提供或多或少的补充知识。为了提高分类精度，提出了一种基于证据推理的多源域自适应决策级组合方法。从不同的源域得到的分类结果通常具有不同的可靠性/权重，这是根据域的一致性来计算的。因此，将多个分类结果在置信函数框架下通过相应的权重进行打折，然后利用登普斯特规则将这些打折后的结果进行组合。为了减少错误，开发了基于邻域的谨慎决策规则，根据组合结果进行类别决策。如果对象的邻域可以（几乎）正确分类，则该对象被分配到单例类。否则，它会谨慎地将几个可能的类分开。通过这样做，我们可以很好地表征分类的部分不精确性，并降低错误风险。这里定义了一个统一的效用值来反映这种分类的好处。这种谨慎的决策规则可以实现最大的统一效用值，因为部分不精确被认为比错误更好。几个真实的数据集被用来测试所提出方法的性能，