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https://irlib.pccu.edu.tw/handle/987654321/35957
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| 题名: | A Hybrid Detecting Fraudulent Financial Statements Model Using Rough Set Theory and Support Vector Machines |
| 作者: | Yeh, CC (Yeh, Ching-Chiang)
Chi, DJ (Chi, Der-Jang)
Lin, TY (Lin, Tzu-Yu)
Chiu, SH (Chiu, Sheng-Hsiung) |
| 贡献者: | 會計系 |
| 关键词: | Fraudulent financial statements
rough set theory
support vector machines |
| 日期: | 2016 |
| 上传时间: | 2017-04-17 13:22:09 (UTC+8) |
| 摘要: | The detection of fraudulent financial statements (FFS) is an important and challenging issue that has served as the impetus for many academic studies over the past three decades. Although nonfinancial ratios are generally acknowledged as the key factor contributing to the FFS of a corporation, they are usually excluded from early detection models. The objective of this study is to increase the accuracy of FFS detection by integrating the rough set theory (RST) and support vector machines (SVM) approaches, while adopting both financial and nonfinancial ratios as predictive variables. The results showed that the proposed hybrid approach (RST+SVM) has the best classification rate as well as the lowest occurrence of Types I and II errors, and that nonfinancial ratios are indeed valuable information in FFS detection. |
| 關聯: | CYBERNETICS AND SYSTEMS 卷: 47 期: 4 頁碼: 261-276 |
| 显示于类别: | [會計學系暨研究所 ] 期刊論文
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