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
Link To Document