博碩士論文 100522009 詳細資訊




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姓名 戴安傑(AN-jie Dai)  查詢紙本館藏   畢業系所 資訊工程學系
論文名稱 OR ensemble 應用於財務危機預測
(Financial crisis prediction based on OR ensemble)
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摘要(中) 財務危機預測問題長久以來都是一個重要且常被廣泛討論的主題,吸引了世界各地的投資者和研究學者的關注。若想要能夠準確地進行預測,選個好的預測模型就顯得十分重要,我們從文獻中可發現,在常見的資料集下,沒有一致推薦的預測模型可供我們解決財務危機預測問題。又最近研究顯示,集成學習(ensemble learning)比單一分類器來的好且穩定,因此本研究以集成學習為基底,設計出一個新的預測模型,期望此預測模型不僅能夠對付現有的財務危機資料集,對於一個未來沒看過的資料集,也能夠準確預測。
摘要(英) Financial crisis problem has been important and widely studied topic. Financial crisis prediction is receiving increasing attention of stakeholders and researchers in the worldwide. If you want to predict accurately, choose a good prediction model is very important, from the literature can be found , in the common data set, there is no consistant conclusion of the prediction model for us to solve the financial crisis prediction problem, and recent studies have shown that ensemble learning is good and stable than a single classifier, thus, this study base on ensemble learning, to design a new prediction model, expect this prediction model not only deal with existing financial crisis data sets, but also predict accurately about a future data sets that we have never seen.
關鍵字(中) ★ 集成學習
★ 分類器
★ 財務危機預測
關鍵字(英) ★ ensemble learning
★ classifier
★ financial crisis prediction
論文目次 目錄
中文摘要 i
Abstract ii
致謝 iii
圖目錄 vi
表目錄 viii
一、緒論 1
1-1.研究背景 1
1-2.研究動機 3
1-3.論文架構 8
二、文獻探討 9
三、Ensemble learning與classifiers 11
3-1. 分類器 11
3-1-1. SVM 11
3-1-2. KNN 15
3-1-3. Neural Network 16
3-1-4. CART 17
3-1-5. Naïve bayes 18
3-2. Ensemble method 19
3-2-1. Bagging 19
3-2-2. Boosting 20
3-2-3. stacking 22
3-2-4. Majority voting 24
四、Proposed an ensemble 26
4-1.組合方式 26
4-2. 選取base learner的方針 27
4-3. OR ensemble架構 27
五、實驗設計 29
5-1.資料來源 29
5-2.資料前處理 32
5-3.Misclassification cost和cost ratios 33
5-4.實驗參數 34
5-5.研究假設 36
5-6.實驗架構 36
六、實驗結果與分析 38
6-1.base learner的挑選 38
6-2.四個資料集的結果 43
6-2-1.台灣資料集 43
6-2-2.中國資料集 48
6-2-3.澳洲資料集 51
6-2-4.德國資料集 55
6-3.結果分析 59
6-4.再次文獻探討 61
七、結論與未來展望 64
7-1.總結 64
7-2.研究貢獻 64
7-3.未來展望 65
參考文獻 67
附錄一 70
附錄二 73
附錄三 79
附錄四 89
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指導教授 梁德容(De-ron Liang) 審核日期 2013-9-12
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