臺灣博碩士論文加值系統

審計人員於企業破產前如未被出具繼續經營意見疑慮，將被視為審計報導失敗。因此，如何正確評估繼續經營意見，對審計人員來說相當重要。近年來相關研究開始以資料探勘建構繼續經營意見決策模式，其中，支援向量機顯示出良好的分類能力。但當輸入變數的維數過高，在運用支援向量機時，會造成無法預料之影響與噪音干擾。為了解決支援向量機現存之缺點，故本研究嘗試整合資料探勘中的流形學習與支援向量機來建構繼續經營意見決策之分類模式，以提高模式的分類正確率之外，並比較主成份分析法、古典多元尺度法、等距離映射法及局部嵌入法等四種流形學習的降維效果。實證結果顯示，本研究建立之整合流形學習與支援向量機的兩階段模式建構程序，能有效的提昇繼續經營意見決策模式的正確率。此外，本研究進一步發現運用等距離映射法進行資料降維，可有效提昇模式的正確率，其次為主成份分析法。但古典多元尺度法與局部線性嵌入法的整合模式之實證結果卻是低於單純使用支援向量機之模式。

It is viewed as audit reporting failures that the auditors don’t issue going concern uncertainty opinion to the companies before bankrupt. It is important how auditors assess going concern of client correctly Prior study about going concern opinion focused on finding factors of going concern opinion. Some researches use data mining techniques to develop going concern opinion model in recent years. Support vector machine (SVM) is good performance of classification, but it often causes dramatic inefficiency and noise when using SVM that input data is high dimension-al data. For solving defects of SVM, we attempt to integrate manifold learning and SVM to construct going concern opinion decision model in order to improve accu-racy of model in our study, and we do a comparison of principal component analysis (PCA), Classical Multidimensional Scaling (cMDS), isometric mapping (ISOMAP), and locally linear embedding (LLE) on dimensionality reduction effect. The empirical result shows that two-stage model of integration of manifold learning and SVM improve effective accuracy for going concern opinion decision model in our study. In addition, we further find that using ISOMAP on dimensionality reduction can improve best accuracy of model, next best is PCA. But accuracy of integration models which use cMDS and LLE are worse than SVM model.

內容目錄
中文摘要　．．．．．．．．．．．．．．．．．．．．． iii
英文摘要　．．．．．．．．．．．．．．．．．．．．． iv
誌謝辭　　．．．．．．．．．．．．．．．．．．．．． v
內容目錄　．．．．．．．．．．．．．．．．．．．．． vi
表目錄　　．．．．．．．．．．．．．．．．．．．．． viii
圖目錄　　．．．．．．．．．．．．．．．．．．．．． ix
第一章　　緒論．．．．．．．．．．．．．．．．．．． 1
　　第一節　　研究背景與動機．．．．．．．．．．．． 1
　　第二節　　研究目的．．．．．．．．．．．．．．． 4
　　第三節　　論文研究架構與流程．．．．．．．．．． 4
第二章　　文獻探討．．．．．．．．．．．．．．．．． 7
　　第一節　　繼續經營意見之定義及文獻探討．．．．． 7
　 第二節　　小結．．．．．．．．．．．．．．．．． 11
第三章　　研究設計．．．．．．．．．．．．．．．．． 15
　　第一節　　研究架構．．．．．．．．．．．．．．． 15
　　第二節　　研究變數．．．．．．．．．．．．．．． 16
　　第三節　　樣本選取與資料蒐集．．．．．．．．．． 20
　　第四節　　研究方法．．．．．．．．．．．．．．． 21
第四章　　實證結果與分析．．．．．．．．．．．．．． 31
　　第一節　　流形學習．．．．．．．．．．．．．．． 31
　　第二節　　模式分析及比較．．．．．．．．．．．． 37
第五章　　結論與建議．．．．．．．．．．．．．．．． 42
　　第一節　　研究結論．．．．．．．．．．．．．．． 42
　　第二節　　研究限制．．．．．．．．．．．．．．． 43
　　第三節　　未來研究建議．．．．．．．．．．．．． 44
參考文獻　．．．．．．．．．．．．．．．．．．．．． 45

表目錄
表 2- 1 繼續經營意見決策模型相關研究彙總表．．．． 13
表 3- 1 研究變數彙總表．．．．．．．．．．．．．． 17
表 4- 1 流形學習參數彙總表．．．．．．．．．．．． 35
表 4- 2 五組交叉驗證之構成樣本．．．．．．．．．． 38
表 4- 3 支援向量機之測試結果．．．．．．．．．．． 39
表 4- 4 整合流形學習與支援向量機模型的測試結果．． 40
表 4- 5 型一及型二錯誤率之彙總表．．．．．．．．． 41

圖目錄
圖 1- 1 論文流程圖．．．．．．．．．．．．．．．． 6
圖 3- 1 研究架構．．．．．．．．．．．．．．．．． 16
圖 3- 2 局部線性嵌入法降維之流程圖．．．．．．．． 26
圖 4- 1 主成份分析特徵值陡坡圖．．．．．．．．．． 32
圖 4- 2 古典多元尺度法特徵值陡坡圖．．．．．．．． 32
圖 4- 3 等距離映射法殘值圖．．．．．．．．．．．． 33
圖 4- 4 局部線性嵌入法殘值圖．．．．．．．．．．． 34
圖 4- 5 主成份分析低維空間分佈圖．．．．．．．．． 35
圖 4- 6 古典多元尺度法低維空間分佈圖．．．．．．． 36
圖 4- 7 等距離映射法低維空間分佈圖．．．．．．．． 36
圖 4- 8 局部線性嵌入法低維空間分佈圖．．．．．．． 37
圖 4- 9 γ及C參數值等高線圖．．．．．．．．．．．． 37

一、中文部分

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財團法人中華民國會計研究發展基金會審計準則委員會(2006)，審計準則公報及審計實務指引合訂本。台北：著者發行，71-78。


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