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研究生:許大鈞
研究生(外文):Ta-Chun Hsu
論文名稱:應用案例式推論與基因演算法於信用評等決策輔助系統
指導教授:周世傑周世傑引用關係
指導教授(外文):Shih-Chieh Chou
學位類別:碩士
校院名稱:國立中央大學
系所名稱:資訊管理研究所
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
論文出版年:2002
畢業學年度:90
語文別:中文
論文頁數:59
中文關鍵詞:案例式推論法基因演算法k最臨近理論個人信用評等
相關次數:
  • 被引用被引用:21
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近年來本國金融機構信用快速擴充,而金融機構的營收重於放款業務,但提高放款業務比例並不能代表銀行利潤的增加,放款的風險為逾期放款,必須有好的放款品質,方不致使逾放比率增加,因此有效控管信用放款的質與量,成為目前各金融機構經營的首重目標。為減少逾放比率、提高放款量、爭取審核時間,利用知識管理之知識再使用(reuse)的審核機制是必需的,客觀科學化的評分方法更能使徵信資料得到迅速的整理與分析,以利信用放款的決策。
本研究以國內銀行業申貸案例為研究對象,採用案例式推論法(Case Based Reasoning)結合基因演算法(Genetic Algorithm)發展信用評等決策輔助系統,探討國內銀行業者使用之個人信用評分表之表列變數,並將歷史資料分為訓練及測試案例,支援本系統學習出最佳的案例屬性權重,應用在案例的擷取過程,以擷取新申貸案例的最相似歷史案例,建立最適之信用評等模式,從而預測新申貸者授信的成敗,並提供相似的案例供信用審核人員進行決策。研究結果顯示:一、大量訓練案例數會有較佳的預測申貸成敗之結果。二、以k最臨近理論,投票案例數為5時,正常案例預測率及滯繳案例預測率皆可超過75%以上。三、使用投票法為低風險低獲利策略,不使用投票法為高風險高獲利策略。
第一章緒論1
第一節研究背景及動機1
第二節研究目的2
第三節研究範圍與假設3
第四節論文架構說明3
第二章文獻探討5
第一節案例式推論(Case Based Reasoning)5
第二節k-Nearest Neighbor Algorithm10
第三節基因演算法(Genetic Algorithms)12
第四節個人信用評等19
第三章研究設計22
第一節系統流程22
第二節結合案例式推論與基因演算法之決策輔助系統架構23
第三節基因演算法設計27
第四節資料蒐集分析32
第四章實驗設計及結果分析40
第一節實驗工具40
第二節基因演算法設定41
第三節實驗設計42
第四節實驗結果43
第五節實驗結果分析51
第五章結論與未來研究方向54
第一節結論54
第二節未來研究方向54
參考文獻56
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