In the applications of domain adaptation (DA), there may exist multiple source domains, and each source domain usually provides some auxiliary information for object classification. The combination of such complementary knowledge from different source domains is helpful for improving the accuracy. We propose an evidential combination of augmented multi-source of information (ECAMI) method. The information sources are augmented at first by merging several randomly selected source domains to generate extra auxiliary information. We can obtain one piece of classification result with the assistance of each information source based on DA. Then these multiple classification results are combined by belief functions theory, which is expert at dealing with the uncertain information. Nevertheless, the classification results derived from different information sources may have different weights. The optimal weights are calculated by minimizing an given error criteria defined by the distance between the combination result and the ground truth using some training data. For each object, the augmented information sources will produce multiple classification results that will be discounted by the learnt weights under the belief functions framework. Then the combination of these discounted results is employed to make the final class decision. The effectiveness of ECAMI is evaluated with respect to some related methods based on several real data sets, and the experimental results show that ECAMI can significantly improve the classification accuracy.

在域自适应（DA）的应用中，可能存在多个源域，并且每个源域通常提供一些用于对象分类的辅助信息。来自不同源域的此类补充知识的组合有助于提高准确性。我们提出了增强的多信息源（ECAMI）方法的证据组合。首先，通过合并几个随机选择的源域以生成额外的辅助信息来增强信息源。在基于DA的各个信息源的帮助下，我们可以获得一个分类结果。然后利用信念函数理论将这些多个分类结果组合起来，这是处理不确定信息的专家。不过，来自不同信息源的分类结果可能具有不同的权重。最佳权重是通过使用一些训练数据最小化由组合结果和地面真实情况之间的距离定义的给定误差标准来计算的。对于每个对象，增强的信息源将产生多个分类结果，这些结果将在信念函数框架下被所学习的权重所打折。然后，将这些折现结果的组合用于做出最终的类别决策。基于多个真实数据集，针对一些相关方法评估了ECAMI的有效性，实验结果表明ECAMI可以显着提高分类准确性。最佳权重是通过使用一些训练数据最小化由组合结果和地面真实情况之间的距离定义的给定误差标准来计算的。对于每个对象，增强的信息源将产生多个分类结果，这些结果将在信念函数框架下被所学习的权重所打折。然后，将这些折现结果的组合用于做出最终的类别决策。基于多个真实数据集，针对一些相关方法评估了ECAMI的有效性，实验结果表明ECAMI可以显着提高分类准确性。最佳权重是通过使用一些训练数据最小化由组合结果和地面真实情况之间的距离定义的给定误差标准来计算的。对于每个对象，增强的信息源将产生多个分类结果，这些结果将在信念函数框架下被所学习的权重所打折。然后，将这些折现结果的组合用于做出最终的类别决策。根据一些实际数据集，针对某些相关方法评估了ECAMI的有效性，实验结果表明ECAMI可以显着提高分类准确性。增强的信息源将产生多个分类结果，这些结果将在信念函数框架下被学习的权重所打折。然后，将这些折现结果的组合用于做出最终的类别决策。基于多个真实数据集，针对一些相关方法评估了ECAMI的有效性，实验结果表明ECAMI可以显着提高分类准确性。增强的信息源将产生多个分类结果，这些结果将在信念函数框架下被学习的权重所打折。然后，将这些折现结果的组合用于做出最终的类别决策。根据一些实际数据集，针对某些相关方法评估了ECAMI的有效性，实验结果表明ECAMI可以显着提高分类准确性。