• DocumentCode
    2427962
  • Title

    An attribute reduction SVM-based tax assessment model

  • Author

    Liu, Han ; Yu, Xiaoqing ; Wan, Wanggen

  • Author_Institution
    Sch. of Commun. & Inf. Eng., Shanghai Univ., Shanghai
  • fYear
    2008
  • fDate
    7-9 July 2008
  • Firstpage
    1167
  • Lastpage
    1171
  • Abstract
    Tax assessment has always been regarded as a tool to check whether taxpayers submit the right amount of money. This paper propose a new tax assessment model mainly based on an attribute reduction SVM in the light of its good performance in classifying high-dimensional nonlinear data. In the paper, we apply our model to a real-world example to see its practical performance. The results show that our model performs well both in data classification accuracy and predictive accuracy.
  • Keywords
    pattern classification; prediction theory; support vector machines; taxation; attribute reduction support vector machine; high-dimensional nonlinear data classification; predictive accuracy; tax assessment model; Accuracy; Artificial intelligence; Classification algorithms; Data mining; Decision trees; Personnel; Predictive models; Statistical learning; Support vector machine classification; Support vector machines; SVM; tax assessment;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Audio, Language and Image Processing, 2008. ICALIP 2008. International Conference on
  • Conference_Location
    Shanghai
  • Print_ISBN
    978-1-4244-1723-0
  • Electronic_ISBN
    978-1-4244-1724-7
  • Type

    conf

  • DOI
    10.1109/ICALIP.2008.4590278
  • Filename
    4590278