類神經網路於營利事業所得稅選案之應用; Application of Neural Networks in Business Income Tax Case Selection

NCUIR > College of Electrical Engineering & Computer Science > Executive Master of Computer Science and Information Engineering > Electronic Thesis & Dissertation > Item 987654321/8627

Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/8627

Title:	類神經網路於營利事業所得稅選案之應用;Application of Neural Networks in Business Income Tax Case Selection
Authors:	吳進照;Jinn-Jaw Wu
Contributors:	資訊工程學系碩士在職專班
Keywords:	類神經網路;逃漏稅;營利事業所得稅;稅務管理;neural networks;tax administration;business income taxt;tax evasion
Date:	2003-07-03
Issue Date:	2009-09-22 11:31:59 (UTC+8)
Publisher:	國立中央大學圖書館
Abstract:	逃漏稅在各國都是一個嚴重的問題，如果個人發覺逃漏稅的益處勝於成本，就有可能逃漏稅，由於稅務查核人力不足，無法逐一查核申報案件，亦即有多少申報案件被選出審查，決定了查核機率，所以高查核機率及高懲罰率通常會導致逃漏稅減少，高查核機率需要比較多資源，這是將面臨的一個難題，因此如何發展一個電腦化的檢測方法，已成為迫切的挑戰，本論文提出一個以類神經網路為基礎的方法，以完成有效檢測系統，首先使用皮爾森係數法、主成分分析法、費雪比率法及統計數量法，這四種不同的特色抽出方法，從申報案件中抽出有效特色，然後再採用多維矩形複合式類神經網路、倒傳遞演算法、學習向量量化網路、二元羅吉斯迴歸及決策樹，以完成檢測系統。稅務資料集取自財政部臺灣省北區國稅局八十七及八十八年度資料，資料集分為包含八十七年度申報案件之訓練資料集及八十八年度申報案件之測試資料集，模擬實驗結果顯示電腦化的檢測系統之性能勝過現有檢測方法，所以這些檢測系統為防止逃漏稅提供了一個替代工具。 Tax evasion is a serious problem in all countries. If the perceived benefits of evasion outweigh the perceived costs then, if it is possible, individuals will evade taxes. Owing to the lack of enough tax officers, reported cases cannot be audit one by one. That is, the audit probability determines how many reported cases will be randomly selected and reexamined. Therefore, high audit probability and high penalty rate usually may lead to a decrease of evasion. A crucial problem to be encountered is that a high audit probability needs high resources. Therefore, how to develop a computerized detection method becomes a very demanding challenge. In this thesis, a neural-network-based method is proposed to implement an efficient detection system. First, four different feature extractors; such as Pearson’s correlation, Principal component analysis, Fisher ratio, and Statistics quantity method are employed to extract effective features from reported cases. Then a Hyper Rectangular Composite Neural Networks(HRCNNs),multi-layer perceptrons(MLPs) with the backpropagation algorithm, learning vector quantization(LVQ)networks, binary logistic regression method, and decision Tree are employed to implement detection systems. The tax data sets were collected from National Tax Administration Northern Province Ministry of Finance in 1998 and 1999, respectively, the data sets were splitted into a training data set consisting of reported cases in 1998 and a testing data set consisting of reported cases in 1999. Simulation results show that the computerized detection systems outperformed the present auditing process. Therefore, these detection systems provide an alternative tool for preventing tax evasion.
Appears in Collections:	[Executive Master of Computer Science and Information Engineering] Electronic Thesis & Dissertation

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