臺灣博碩士論文加值系統

過去應用資料探勘技術於破產預測領堿的研究紛紛建構出不同的預測模型，然而在混合式的模型當中，在資料前處理的部分都是著重在屬性篩選上，較少是針對資料精減的研究。適當的資料精減可以淨化訓練用的資料，將較特異且不具代表性的資料刪除，使訓練出來的模型有較好的預測效能。因此本研究運用K-means分群技術可以找出資料群集之中心點的性質，建立了一個資料精減的方法。藉由計算出所有資料與中心點之距離，再按照不同的比例將距離中心點較遠的資料刪除來達到資料精減的效果。我們使用破產預測領域常用的四個公開下載的資料集，在資料精減掉不同比例的資料量後，分別以類神經網路、決策樹、羅吉斯迴歸等四種分類技術來訓練模型。研究結果顯示，按照不同比例精減過的資料集用四種不同的分類技術所訓練出來的模型，其經過交叉驗證後的平均正確率大多高於資料精減前的結果。而隨著資料精減的比例增加，精減掉離中心點距離值較遠的資料，得到較乾淨的資料集後，平均正確率也有上升的趨勢。

Prior researches of using data mining techniques in bankruptcy prediction focus mainly on constructing effective prediction models. Particularly, many of them develop hybrid models based on feature selection in the pre-processing stage. However, very few of them emphasize on data reduction. Data reduction can make the training dataset cleaner and reduce outlier data, which can improve prediction accuracy. Therefore, the purpose of this thesis is to build up a data reduction method by using K-means to find the center of each cluster, and calculate the distance from all the data in a specific cluster to its cluster center. Then, we reduce the farther data in different percentages as the outlier data. We use four commonly used datasets in the bankruptcy prediction domain and employ neural networks, decision trees, logistic regression, and support vector machines as the prediction models after data reduction. The experimental results show that when the model trained by the four classifiers using four datasets after data reduction, the accuracy in general is higher than the model without data reduction. Moreover, the accuracy becomes higher when the reduction percentage increases.

謝 辭 i
摘 要 ii
目　錄 iv
圖目錄 vi
表目錄 vii
第一章 緒論 1
1.1 研究背景與動機 1
1.2 研究目的 4
1.3 論文架構 5
1.4 論文流程 6
第二章 文獻探討 7
2.1 破產預測 7
2.2 資料探勘 8
2.2.1 資料探勘之定義 8
2.2.2 資料探勘的方式 9
2.2.3 資料探勘的步驟 11
2.3 前處理(Pre-Processing) 12
2.3.1 資料精減(Data Reduction) 13
2.3.2 屬性篩選(Feature Selection) 14
2.4 分類技術(Classification) 14
2.4.1 類神經網路(Neural Network) 15
2.4.2 決策樹(Decision Tree) 17
2.4.3 支援向量機(Support Vector Machines) 19
2.4.4 羅吉斯迴歸(Logistic Regression) 21
2.5 分群技術(Clustering) 22
2.5.1 K-means 24
2.6 應用資料探勘技術於破產預測之相關文獻 26
第三章 研究方法 33
3.1 研究設計及架構 33
3.2 資料來源 35
3.3 資料精減之流程及方法 36
3.4 預測模型之建立 38
3.4.1 預測模型建立流程 38
3.4.2 10-Fold交叉驗證 39
3.4.3 分類技術之相關設定 40
3.5 模型評估方法 42
第四章 實驗結果與分析 43
4.1 各模型之平均正確率分析與比較 43
4.1.1 Baseline之平均正確率 43
4.1.2 按不同比例資料精減後之平均正確率 44
4.1.3 平均正確率分析 46
4.2 型I、型II錯誤率 50
4.3 延伸討論 59
4.3.1 距離值分析探討 59
4.3.2 各資料集平均正確率最高之精減比例討論與驗證 62
第五章 研究結論與建議 65
5.1 研究結論 65
5.2 研究貢獻與建議 66
5.2.1 研究貢獻 66
5.2.2 未來研究方向與建議 67
參考文獻 68

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