Abstract
Recent research on transfer learning reveals that adversarial domain adaptation effectively narrows the difference between the source and the target domain distributions, and realizes better transfer of the source domain knowledge. However, how to overcome the intra/inter-domain imbalance problems in domain adaptation, e.g. observed in cross-domain credit risk forecasting, is under-explored. The intra-domain imbalance problem results from the extremely limited throngs, e.g., defaulters, in both source and target domain. Meanwhile, the disparity in sample size across different domains leads to suboptimal transferability, which is known as the inter-domain imbalance problem. In this paper, we propose an unsupervised purifier training based transfer learning approach named ADAPT (Adversarial Domain Adaptation with Purifier Training) to resolve the intra/inter-domain imbalance problems in domain adaptation. We also extend our ADAPT method to the multi-source domain adaptation via weighted source integration. We investigate the effectiveness of our method on a real-world industrial dataset on cross-domain credit risk forecasting containing 1.33 million users. Experimental results exhibit that the proposed method significantly outperforms the state-of-the-art methods. Visualization of the results further witnesses the interpretability of our method.
G. Zeng, J. Chi—Contributed equally.
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Acknowledgment
The research work supported by Alibaba Group through Alibaba Innovative Research Program and the National Natural Science Foundation of China under Grant (No.61976204, 92046003, U1811461). Xiang Ao is also supported by the Project of Youth Innovation Promotion Association CAS, Beijing Nova Program Z201100006820062.
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Zeng, G., Chi, J., Ma, R., Feng, J., Ao, X., Yang, H. (2022). ADAPT: Adversarial Domain Adaptation with Purifier Training for Cross-Domain Credit Risk Forecasting. In: Bhattacharya, A., et al. Database Systems for Advanced Applications. DASFAA 2022. Lecture Notes in Computer Science, vol 13245. Springer, Cham. https://doi.org/10.1007/978-3-031-00123-9_29
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