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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Combination of Transferable Classification With Multisource Domain Adaptation Based on Evidential Reasoning
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Combination of Transferable Classification With Multisource Domain Adaptation Based on Evidential Reasoning

机译：基于证据推理的多源域适应的可转移分类组合

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摘要

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.
机译：在域自适应的应用中，可能存在多个源域，其可以在目标域中提供更多或多或少的互补知识。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。从不同源极域获得的分类结果通常具有不同的可靠性/权重，根据域一致性计算。因此，多种分类结果由信仰功能框架下的相应权重折扣，然后，Dempster的规则被用来将这些折扣结果组合起来。为了减少错误，开发了一种基于邻域的谨慎决策规则，以根据组合结果进行课程决定。如果可以（几乎）正确分类，则该对象被分配给单例类。否则，谨慎致力于几个可能的课程的分离。通过这样做，我们可以很好地描述分类的部分不精确，并降低错误风险。这里定义了统一的实用程序值来反映此类分类的好处。这种谨慎的决策规则可以实现最大统一的实用程序值，因为部分不精确被认为是比错误更好。几种真实数据集用于测试所提出的方法的性能，实验结果表明，我们的新方法可以有效地提高关于其他相关组合方法的分类准确性。

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