Evidential combination of augmented multi-source of information based on domain adaptation

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.