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姓名 張懷倫(Huai-lun Chang)  查詢紙本館藏   畢業系所 軟體工程研究所
論文名稱 結合領域知識與機器運算之新的特徵選取方法: 應用於財務危機預警預測之問題
(Novel feature selection methods to Financial Distressed Prediction problem)
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摘要(中) 在目前眾多的研究議題中,特徵選取(Variable and feature selection)已經是一個越來越令人關注的議題。尤其是當我們收集樣本的特徵集(Feature sets)成千上百的增加的時候,一個好的特徵選取方法可以使得結果令人滿意。
本論文提出了一個概念,此概念是嘗詴去結合專家意見(Expert recommendation)與機器學習演算法(Machine Learning Algorithm)後,創造出一種混合型的特徵選取方法(Novel feature selection methods),並且使用預測財務危機公司(Financial Distressed Prediction,簡稱FDP)此問題當作案例做為實驗證實。
本論文的貢獻在於對於特徵選取這個議題而言,我們提供了兩個新的方法:Advanced wrapper method & Mix of Expert and Machine(MEM)。而這兩個方法對於應用在非結構化的商業問題上(unstructured nature of the business problems)有著比貣以往的方法更佳的結果-擁有更勝於以往的預測準確率以及為數少量的推薦特徵集。
摘要(英) Variable and feature selection is an important issue in plenty of issues, especially feature sets is growing up violently. A good variable and feature selection will have bearing on performance of result.
In this paper, we apply a new concept that combines expert recommendation and machine learning algorithm to create a novel feature selection, and utilize the financial distress prediction problem as a study case to prove our idea.
We apply two methods that Advanced wrapper method & mixed of expert and machine (MEM) to applicate in nonstructed business problem and believe this proposed methods be better performance than original methods included predictor accuracy and few feature set.
關鍵字(中) ★ 遺傳演算法
★ 財務危機預測
★ 特徵選取
關鍵字(英) ★ genetic algorithm
★ wrapper method
★ Financial Distressed Prediction
★ Feature Selection
論文目次 摘要................................................................................................................................ I
Abstract ......................................................................................................................... II
致謝.............................................................................................................................. III
目錄............................................................................................................................ VII
圖目錄.......................................................................................................................... IX
表目錄........................................................................................................................... X
1 緒論........................................................................................................................ 1
1.1. 研究背景.................................................................................................... 1
1.2. 研究動機.................................................................................................... 3
1.3. 問題定義(Problem Definition) .................................................................. 8
2. 文獻探討.............................................................................................................. 10
2.1. Feature Selection相關文獻探討 ............................................................. 10
3. Advanced wrapper method .................................................................................. 13
3.1. Advanced wrapper method演算法概念與流程。 ................................. 13
3.2. 實驗A. 前提假設、實驗組與對照組 ................................................... 17
3.3. 實驗A. 結果分析 ................................................................................... 20
4. Mixed of Expert and Machine (MEM) ................................................................ 23
4.1. MEM演算法概念與流程 ....................................................................... 23
4.2. 實驗B. 前提假設、實驗組與對照組 ................................................... 33
4.3. 實驗B. 結果分析 ................................................................................... 36
5. 結論...................................................................................................................... 39
6. 未來展望.............................................................................................................. 40
7. 參考文獻.............................................................................................................. 42
8. 附錄...................................................................................................................... 46
8.1. 附錄A 實驗公司樣本 ............................................................................ 46
8.2. 附錄B. 初始特徵集 ............................................................................... 53
8.3. 附錄C. 著名論文所推薦之特徵集集合 ............................................. 64
8.4. 附錄D Feature set by expert clustering .................................................. 66
8.5. 附錄E. MEM_Ttest在不同階段所提供之最終推薦特徵集................ 70
8.6. 附錄F. 實驗B.詳細數據(包含準確率、Type I Error) ........................ 71
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指導教授 梁德容(De-ron Liang) 審核日期 2011-7-21
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