臺灣博碩士論文加值系統

財務報表提供了投資人了解企業經營的成果以及財務狀況，但在近年來財務報表問題案例頻傳，影響的不僅僅是投資人無法依據財務報表做出即時判斷更消耗了社會許多資源，因此本研究利用了財務資料以及透明度判斷財務報表是否擁有審計風險，其中使用支援向量機預測財務報表審計風險的可能性，接著利用SVM+Prototypes對支援向量機提取出規則，最後利用兩種分群演算法Fuzzy C-means以及自我組織映射希望能加以改良SVM+Prototypes中的Prototype，在研究顯示出利用Fuzzy C-means較能有效的提高準確率以及覆蓋率，而利用自我組織映射則幫助不大，因此可利用較佳的規則判斷某家企業之財務報表是否擁有審計的風險以及解釋，並給予財務報表使用者一個判斷的根據。

The financial statements provide investors understand the business of operating results and financial position. In recent years, the financial statements of problem are frequent and affect not only the investor can’t make immediate judgment based on the financial statements but also consumption of many resources of the community. This study use financial information and transparency to judge the financial statements whether has audit risk, first using support vector machine to predict the risk of financial statement audits, then use the SVM + Prototypes for support vector machines to extract rules. Finally, using Fuzzy C-means and self-organizing map hope can be improved the SVM + Prototypes. Researching has shown that using Fuzzy C-means more effective to increase the accuracy and coverage but self-organizing map does not. A judgment in accordance with better rule to determine a company's financial statements whether has the audit risk and explain, then give a basis for judgment to the users of financial statements.

目錄
誌謝 ii
中文摘要 iii
Abstract iv
目錄 v
圖目錄 vi
表目錄 vii
第一章 緒論 1
第二章 文獻探討 6
2.1財務報表問題議題之研究 6
2.2差分演算法 10
2.3支援向量機 13
2.4 SVM+Prototypes 15
2.5 Fuzzy C-means 17
2.6自我組織映射(Self-Organizing Map, SOM) 19
第三章 分群演算法 22
3.1分群演算法簡介 22
3.2階層式分群法 23
3.3 K-means 26
3.4自我組織映射 28
3.5 Fuzzy c-means 32
第四章 研究方法 34
4.1差分演化法 34
4.2基因演算法 37
4.3支援向量機 43
4.4 SVM+Prototypes 48
4.5 SVM+ fuzzy C-means Prototypes 、 SVM+ SOM Prototypes 54
第五章 資料分析 56
第六章 研究架構與實驗結果 63
6.1研究架構 63
6.2實驗結果 64
圖目錄
圖一 促使財務報表問題發生的三要素 4
圖二 階層式分裂演算法以及階層式聚合演算法 23
圖三 單一鏈結演算法 23
圖四 完整鏈結演算法 24
圖五 中心法 24
圖六 K-means流程圖 27
圖七 K-means示意圖 28
圖八 類神經網路架構 29
圖九 墨西哥帽函數 30
圖十 自我組織映射鄰近區域 31
圖十一 Fuzzy C-means、K-means之隸屬函數 33
圖十二 差分演化法流程圖 37
圖十三 基因演算法流程圖 38
圖十四 輪盤法 40
圖十五 單點交配法 41
圖十六 雙點交配法 42
圖十七 多點(遮罩)交配法 42
圖十八 二進制編碼突變 42
圖十九 實數編碼突變 43
圖二十 比較最佳區分超平面 44
圖二十一 支援向量機結構 45
圖二十二 高維度轉變為低維度 47
圖二十三 支援向量機截取規則四大類 49
圖二十四 SVM+Prototypes兩種規則 50
圖二十五 超矩形一分為二 53
圖二十六 橢圓與超矩形之差異 54
圖二十七 挑選屬性 61
圖二十八 研究流程圖 64
圖二十九 研究結果:準確率加覆蓋率 68
圖三十 分群演算法基於離群值之比較 71
圖三十一 分群演算法基於重複值之比較 71
表目錄
表一 財務報表問題議題之文獻回顧 7
表二 差分演化法之文獻回顧 11
表三 支援向量機之文獻回顧 14
表四 SVM+Prototypes之文獻回顧 16
表五 Fuzzy C-means之文獻回顧 18
表六 自我組織映射之文獻回顧 20
表七 階層式分群演算法之優缺點 26
表八 基因演算法編碼方式 39
表九 財務比例 59
表十 支援向量機預測準確率 65
表十一 研究結果:準確率 66
表十二 研究結果:覆蓋率 66
表十三 研究結果:準確率加覆蓋率 67
表十四 研究結果:規則數、運算時間 68
表十五 規則表示 69

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