臺灣博碩士論文加值系統

信用風險評估在近年來已成為金融風險管理中重要的課題，而信用評分模型為銀行降低信貸風險並減少呆帳產生之損失的常見方法，在過去研究中大多使用羅吉斯迴歸、決策樹和類神經網路建構模型，而擁有不錯分類能力的支援向量機在近年來也成為常見方法之一，但支援向量機有其缺點，在參數選擇需耗費相當大的時間與成本，且模型不具有解釋能力，因此本研究期望使用隨機子空間之概念結合支援向量機分類器建構信用評分模型，並在提升分類器表現的同時，尋找出重要之變數。本研究使用三家銀行信用卡資料集作為實驗資料，同時為了驗證本研究提出之方法有效性，並與支援向量機、羅吉斯迴歸、分類迴歸樹、類神經網路和隨機森林等分類方法相比較，實驗結果顯示本研究提出之方法擁有較高之分類正確率，並且計算出變數之重要性，有助於金融機構在審核信用卡時判斷顧客違約之可能性。

Credit risk assessment has become one of the most important issues in financial risk management. Analysts generally adopt a credit scoring model to insure the quality of admission evaluation and reduce the credit risk. Some researchers have used logistic regression, decision trees, and artificial neural network to build a credit scoring model. In addition, support vector machine is a powerful classification method with successful utilization in various fields. However, there parameter selection process of SVM is very time-consuming and SVM is a lack of interpretive ability method. Therefore, this study proposes a method to combine random subspace method and SVM to build the credit scoring model. Three credit datasets are selected as experimental data to demonstrate the accuracy of the proposed method. Experimental results show that the proposed method has highest accuracy and reveals the importance of variables effectively. It helps financial institutions to discriminate customers into groups with different risk patterns.

第壹章 緒論 1
第一節 研究背景 1
第二節 研究動機與目的 2
第三節 研究流程 4
第貳章 相關方法與文獻探討 6
第一節 信用評等常見之分類法 6
壹、羅吉斯迴歸 6
貳、分類迴歸樹 7
參、隨機森林 8
肆、類神經網路 8
伍、支援向量機 10
第二節 信用評等之相關研究 14
第參章 研究提出之方法 17
第一節 問題闡述 17
第二節 解決方法 18
壹、隨機子空間法 18
貳、支援向量機方法之參數選擇 19
參、部分隨機子空間支援向量機 21
第三節 指標 24
第肆章 實證分析 26
第一節 資料描述 26
第二節 實驗設計 27
第三節 分析結果 28
壹、資料一之分析結果 29
貳、資料二之分析結果 33
參、資料三之分析結果 37
第伍章 結論與建議 41
參考文獻 43
附錄一 信用卡業務統計表 47
附錄二 信用卡資料集變數 48
附錄三 資料實證之結果 56

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