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題名:資料探勘於信用卡顧客行為評分模型之建構
作者:謝弘一
作者(外文):Hsieh, Horng-I
校院名稱:輔仁大學
系所名稱:商學研究所
指導教授:李天行
學位類別:博士
出版日期:2011
主題關鍵詞:行為評分多元分類支援向量機馬氏田口系統behavioral scoringmulticlass classificationsupport vector machineMahalanobis-Taguchi system
原始連結:連回原系統網址new window
相關次數:
  • 被引用次數被引用次數:期刊(3) 博士論文(0) 專書(0) 專書論文(0)
  • 排除自我引用排除自我引用:3
  • 共同引用共同引用:0
  • 點閱點閱:49
自從雙卡問題及次級房貸風暴浮現,銀行業普遍體認信用風險控管的重要性。銀行現行核卡制度漸嚴,顧客之開發、管理與維持愈顯重要,建構穩健信用風險管理系統已成當務之急。然銀行內部龐大顧客資料,無法單採人工分析,資料探勘可協助萃取隱藏於資料中之重要資訊,進而用於企業經營或行銷策略擬定。本研究旨在利用資料探勘工具,建構信用卡顧客行為評分模型。此外針對支援向量機於大型資料庫訓練的不足之處,亦提出以馬氏田口系統作為訓練子集合之案例篩選工具,以解決支援向量機訓練耗時問題。
實證結果發現,C5.0各項整體鑑別績效指標與各類別鑑別率皆優於其他工具,因此可列為挑選行為評分模型建構工具之優先考量。此外,利用馬氏田口系統之「區間內本類聯集」方案進行SVM訓練案例篩選,除可有效解決SVM訓練耗時問題,更可能進一步提升其正確鑑別率,因此建議於支援向量機模式訓練時,可將馬氏田口系統用於訓練案例之篩選,以有效縮減訓練時間並增加鑑別績效。
Credit risk management has seen growing importance recently due to the impact of several financial crises. The objective of the proposed study is to explore the performance of behavioral scoring using five commonly discussed data mining techniques. Using grid-search method in support vector machine model selection is very time-consuming, especially in large datebases. It would be convenient if large data sets could be replaced by only a subset of informative cases. This study proposed a hybrid Mahalanobis-Taguchi system and support vector machine method to address the potential model selection issue of support vector machine.
To demonstrate the effectiveness of behavioral scoring using data mining techniques, behavioral scoring tasks are performed on one bank credit card dataset in Taiwan. As the results reveal, C5.0 outperforms discriminant analysis, multinomial logistic regression, back-propagation network, and support vector machine in terms of scoring accuracy, and hence is an efficient alternative in implementing behavior scoring tasks. In addition, this proposed Mahalanobis-Taguchi system and support vector machine method not only improves the accuracy but also substantially reduces the training time, thus it can be used in support vector machine model selection procedure especially when large databases are used.
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