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論文中文名稱:整合流形學習與隨機森林於企業危機預警之研究 [以論文名稱查詢館藏系統]
論文英文名稱:The Integrated Methodology of Manifold Learning and Random Forest for Business Distress Prediction [以論文名稱查詢館藏系統]
英文姓名:Meng-Yuan Lee
英文關鍵詞:Manifold learningRandom forestBusiness distressSupport vector machines
論文英文摘要:To construct of the business distress detection model has long been regarded as an important and widely studied issue in the academic and business community. Nowadays, there have been many successful applications of support vector machines (SVM) in business distress detection and data mining problems, where SVM-based models frequently received state-of-the-art results. Recently, random forest (RF) developed by Breiman, have gained popularity due to many attractive features and excellent generalization performance on a wide range of problems. Nevertheless, there has been little work on the application of RF for finance literature. In this paper, we propose a novel model to integrate manifold learning with RF technique, to crease the accuracy for the prediction of business distress. By manifold learning techniques, we can reduce this high dimensional business distress data into a much lower dimensional space, is utilized as a preprocessor to improve business distress prediction capability by RF. The results show that the proposed model provides better classification results than pure RF and support vector machine, can help investors in correct investment decisions.
論文目次:摘 要 i
誌 謝 iii
目錄 iv
表目錄 v
圖目錄 vi
第一章 緒論 1
1.1 研究動機 1
1.2 研究目的 3
1.3 研究架構 4
第二章 文獻探討 6
2.1 企業危機 6
2.2 企業危機預警模式 9
2.3 流形學習 13
2.4 隨機森林 20
2.5 支持向量機 23
第三章 研究方法 27
3.1 研究樣本與變數選取 28
3.2 模型建立 31
第四章 實證研究 33
4.1 研究結果 33
第五章 結論與建議 46
5.1 結論 46
5.2 建議 47
參考文獻 48
附錄 52
A危機與正常公司配對表 52
論文參考文獻:[1] 李天行、唐筱菁,「整合財務比率與智慧資本於企業危機診斷模式之建構-類神經網路與多元適應性雲形迴歸之應用」,資訊管理學報,第十一卷,第二期,1994,第161-189頁。
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