A Tax Evasion Detection Method Based on Positive and Unlabeled Learning with Network Embedding Features

Lingyun Mi, Bo Dong, Bin Shi, Qinghua Zheng. A Tax Evasion Detection Method Based on Positive and Unlabeled Learning with Network Embedding Features. In Haiqin Yang, Kitsuchart Pasupa, Andrew Chi-Sing Leung, James T. Kwok, Jonathan H. Chan, Irwin King, editors, Neural Information Processing - 27th International Conference, ICONIP 2020, Bangkok, Thailand, November 23-27, 2020, Proceedings, Part II. Volume 12533 of Lecture Notes in Computer Science, pages 140-151, Springer, 2020. [doi]

@inproceedings{MiDSZ20,
  title = {A Tax Evasion Detection Method Based on Positive and Unlabeled Learning with Network Embedding Features},
  author = {Lingyun Mi and Bo Dong and Bin Shi and Qinghua Zheng},
  year = {2020},
  doi = {10.1007/978-3-030-63833-7_12},
  url = {https://doi.org/10.1007/978-3-030-63833-7_12},
  researchr = {https://researchr.org/publication/MiDSZ20},
  cites = {0},
  citedby = {0},
  pages = {140-151},
  booktitle = {Neural Information Processing - 27th International Conference, ICONIP 2020, Bangkok, Thailand, November 23-27, 2020, Proceedings, Part II},
  editor = {Haiqin Yang and Kitsuchart Pasupa and Andrew Chi-Sing Leung and James T. Kwok and Jonathan H. Chan and Irwin King},
  volume = {12533},
  series = {Lecture Notes in Computer Science},
  publisher = {Springer},
  isbn = {978-3-030-63833-7},
}