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Article Dans Une Revue IEEE Transactions on Neural Networks and Learning Systems Année : 2021

Combination of Transferable Classification with Multi-source Domain Adaptation Based on Evidential Reasoning

Résumé

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 multi-source domain adaptation based on evidential reasoning. The classification results obtained from different source domains usually have different reliabilities/weights, which are calculated according to the domain-consistency. So 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 proposed method, and the experimental results show that our new method can efficiently improve the classification accuracy with respect to other related combination methods.
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Dates et versions

hal-02944558 , version 1 (21-09-2020)

Identifiants

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Zhun-Ga Liu, Lin-Qing Huang, Kuang Zhou, Thierry Denoeux. Combination of Transferable Classification with Multi-source Domain Adaptation Based on Evidential Reasoning. IEEE Transactions on Neural Networks and Learning Systems, 2021, 32 (5), pp.2015-2029. ⟨10.1109/TNNLS.2020.2995862⟩. ⟨hal-02944558⟩
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