Volume 28, Issue 45 (2020)
J Tax Res 2020, 28(45): 59-87 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
A Model for Tax Evasion Forcasting based on ID3 Algorithm and Bayesian Network. J Tax Res 2020; 28 (45) :59-87
URL: http://taxjournal.ir/article-1-1820-en.html
URL: http://taxjournal.ir/article-1-1820-en.html
Abstract: (2821 Views)
Nowadays, knowledge is a valuable and strategic source as well as an asset for evaluation and forecasting. Presenting these strategies in discovering corporate tax evasion has become an important topic today and various solutions have been proposed. In the past, various approaches to identify tax evasion and the like have been presented, but these methods have not been very accurate and the overhead of calculations has also been high. Hence, in this study, a solution is proposed that is based on a combination of the three methods of ID3, Bayesian network and SVM algorithm. In this research, the hybrid RAF set algorithm and hierarchical decision algorithm are used for pre-processing and selecting effective data. The proposed solution in Visual Studio environment using C # programming language and help from Veka library has been compared with popular methods such as ID3, Bayesian and SVM and it is found that this method has much higher accuracy than other methods. The case has been investigated and this indicates the robustness of the proposed method compared to the methods investigated.
Type of Study: Research |
Subject:
Economic
Received: 2020/09/28 | Accepted: 2020/05/30 | Published: 2020/05/30
Received: 2020/09/28 | Accepted: 2020/05/30 | Published: 2020/05/30
| Rights and permissions | |
 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
