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TML-DA: Transfer metric learning based distribution alignment framework for domain class imbalanced classification
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DOI：10.1016/j.knosys.2025.113086.png)
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Abstract
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Nonrandom and biased sampling can lead to class imbalance and distribution mismatch issues between domains. Most existing methods sequentially address these issues by combining resampling strategies with transfer learning. However, these approaches typically focus only on the class imbalance within a single domain and overlook the imbalance across domains. To tackle both domain class imbalance and distribution mismatch problems, this paper proposes a transfer metric learning-based distribution alignment (TML-DA) framework, designed for homogeneous and transductive transfer learning. First, the importance-based transfer metric learning module constructs a transfer metric network with an importance parameter, which learns domain- invariant feature representations of source and target data under the domain class imbalance by increasing its within-class coherence and between-class difference. Then, the target domain label prediction module predicts more accurate labels for the unlabeled target data by leveraging inter-domain distance similarity, offering an improvement over traditional probabilistic prediction methods. Finally, the domain distribution alignment module minimizes both marginal and conditional distribution discrepancies while maximizing within-class coherence and between-class difference. This ensures that the learned transfer metric network generalizes more effectively from the source to the target domain. The proposed TML-DA has been evaluated on the long-tailed USPS+MNIST and Office+Caltech public datasets, delivering superior performance and generalization ability in addressing the domain class imbalance classification of the unlabeled target domain data.
Keywords:
Domain class imbalance
Distribution mismatch
Transfer metric learning
Distribution alignment
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