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- W2892035339 abstract "Tax evasion detection plays a crucial role in addressing tax revenue loss. Many efforts have been made to develop tax evasion detection models by leveraging machine learning techniques, but they have not constructed a uniform model for different geographical regions because an ample supply of training examples is a fundamental prerequisite for an effective detection model. When sufficient tax data are not readily available, the development of a representative detection model is more difficult due to unequal feature distributions in different regions. Existing methods face a challenge in explaining and tracing derived results. To overcome these challenges, we propose an Inter-Region Tax Evasion Detection method based on Transfer Learning (IRTED-TL), which is optimized to simultaneously augment training data and induce interpretability into the detection model. We exploit evasion-related knowledge in one region and leverage transfer learning techniques to reinforce the tax evasion detection tasks of other regions in which training examples are lacking. We provide a unified framework that takes advantage of auxiliary data using a transfer learning mechanism and builds an interpretable classifier for inter-region tax evasion detection. Experimental tests based on real-world tax data demonstrate that the IRTED-TL can detect tax evaders with higher accuracy and better interpretability than existing methods." @default.
- W2892035339 created "2018-09-27" @default.
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- W2892035339 date "2018-08-01" @default.
- W2892035339 modified "2023-09-25" @default.
- W2892035339 title "IRTED-TL: An Inter-Region Tax Evasion Detection Method Based on Transfer Learning" @default.
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- W2892035339 doi "https://doi.org/10.1109/trustcom/bigdatase.2018.00169" @default.
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