• DocumentCode
    3735323
  • Title

    The computational rules extractor in the detection of tax evasion

  • Author

    Yi-Zeng Hsieh;Mu-Chun Su;Addison Y.S. Su;Wu-Rong Shih;Jen-Chih Yu;Chien-Yeh Huang

  • Author_Institution
    Department of Management and Information Technology, Southern Taiwan University of Science and Technology, Taiwan
  • fYear
    2015
  • Firstpage
    181
  • Lastpage
    184
  • Abstract
    It is a serious problem of tax evasion in all domains. Because of the narrow human factors, there are case-by-case basis not to examine reported tax cases. Hence, it is a very demanding challenge to grow an effective tax evasion detection mechanism. In this paper, we use the proposed rule extractor approaches (PFHRCNN) to detect tax evasion. The experiment results show the proposed PFHRCNN for tax evasion detection systems is a good way.
  • Keywords
    "Neural networks","Feature extraction","Companies","Testing","Principal component analysis","Optimization"
  • Publisher
    ieee
  • Conference_Titel
    Security Technology (ICCST), 2015 International Carnahan Conference on
  • Print_ISBN
    978-1-4799-8690-3
  • Electronic_ISBN
    2153-0742
  • Type

    conf

  • DOI
    10.1109/CCST.2015.7389679
  • Filename
    7389679
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3735323