運用決策樹演算法於營業稅選案之研究

Research on Selecting Cases of Business Tax by Applying Decision Tree Technique

胡萬能(Wan-Neng Hu)

指導教授 ： 盧以詮

元智大學/資訊學院/資訊管理學系/碩士(2011年)

摘要

國家的興衰，完全仰賴該國的財政經濟強盛與否，而稅課收入為我國歲入主要財源，又營業稅為各稅目的「火車頭」，因此如何有效的防杜逃漏營業稅已成為稽徵機關重要的課題。營業稅自民國八十八年起改為國稅至今，已累積相當龐大的稅務資料，本研究運用資料探勘技術，以SPSS公司之Clementine 12.0套裝軟體及選擇決策樹演算法中C&R Tree、C5.0及CHAID三種方法進行資料探勘的模型建置，俾利找出最佳的規則及預警模式，以改善選案正確率，提升查核效率。經實證結果決策樹三種演算法中，發現以C5.0所建置之模型，預測的選案正確率最高，亦即可運用資訊科技來協助稽徵機關有效提高選案正確率，降低稽徵成本。

關鍵字

資料探勘 ； 決策樹 ； 虛設行號

並列摘要

Tax revenue is the major income gained by the government. Business tax income, however, is the major source of total tax revenue. Consequently, it is the first priority for taxation agency to curb business tax evasion effectively. As the business tax fillings has been transformed and stored in the central database since 1999, it is desirable and visible to apply information technologies for detecting tax evasion. This study applied data mining techniques, C&R Tree, C5.0 and CHAID, to find out the optimal patterns and models in the case selection for further intervention and investigation by humans. After carrying out the empirical research, in terms of the accuracy, the study recommended that C5.0 is much better than other data mining techniques used in this study. It seems that taxation agency will improve the efficiency of the manpower if data mining techniques are introduced into the tax evasion detection and investigation processes.

並列關鍵字

Data mining ； Decision Tree ； Fictitious Company

參考文獻

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