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Student Number 985205003
Author Chao-hui Wu(吳昭慧)
Author's Email Address No Public.
Statistics This thesis had been viewed 423 times. Download 0 times.
Department Software Engineer
Year 2010
Semester 2
Degree Master
Type of Document Master's Thesis
Language zh-TW.Big5 Chinese
Title On Multiple Classifiers to Financial Distress Prediction
Date of Defense 2011-07-01
Page Count 64
Keyword
  • Data Mining
  • Financial Prediction
  • Machine Learning
  • Multiple Classifier
  • Abstract How to effectively predict financial distress is an important issue in corporate financial management. We use data mining and machine learning methodology to analysis financial statement or financial ratio. Traditional approaches usually formalize financial prediction problem as two-class problem, attempting to differentiate the financially distressed companies (the distressed class) from the normal companies (the non-distressed class). However, there are many factors contributing to a company’s financial crisis. Taiwan Stock Exchange Corporation (TWSE) defines several kinds of financial crisis which show distinct reason. This observation motivates us to further segment the distressed class into a few subclasses. Each subclass corresponds to one crisis type. We propose new methods to design multiple classifier system. Each classifier for a subclass gives a meaningful training set and feature set. It makes that each classifier is professional for each sub-problem. This model is different from the existing approaches that each classifier is not designed for the same pattern recognition problem. The prediction accuracy is superior to traditional approaches by using our prediction model.
    Table of Content 中文摘要i
    Abstractii
    誌謝iii
    目錄iv
    圖目錄vi
    表目錄vii
    一、緒論1
    1-1 研究背景1
    1-2 研究動機與目的3
    1-3 論文架構4
    二、文獻探討5
    2-1 多分類器組合系統5
    2-2 分類器的使用6
    三、實驗架構與設計7
    3-1 實驗公司樣本8
    3-2 實驗資料前置處理(Data Preprocessing)9
    3-3 特徵集合與特徵挑選(Feature Selection)10
    3-4 SVM分類器的參數11
    3-5 挑選推薦特徵組合與參數的迭代方式12
    3-6 驗證模型的方式14
    3-7 前測理論15
    3-8 SVM多分類器組合17
    四、實驗結果以及比較分析19
    4-1 實驗結果20
    4-2 結果分析24
    4-3 延伸討論26
    五、結論與未來展望29
    5-1 結論與貢獻29
    5-2 未來展望30
    參考文獻32
    附錄一 台灣實驗公司樣本與特徵集36
    附錄二 台灣實驗數據總表51
    附錄三 各分類器挑選出的特徵整理54
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    Advisor
  • De-ron Liang(梁德容)
  • Files
  • 985205003.pdf
  • approve in 3 years
    Date of Submission 2011-07-21

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