教師資料查詢 | 類別: 會議論文 | 教師: 戴敏育 Min-Yuh Day (瀏覽個人網頁)

標題:A Comparative Study of Data Mining Techniques for Credit Scoring in Banking
學年102
學期1
發表日期2013/08/14
作品名稱A Comparative Study of Data Mining Techniques for Credit Scoring in Banking
作品名稱(其他語言)
著者Huang, Shih-Chen; Day, Min-Yuh
作品所屬單位淡江大學資訊管理學系
出版者IEEE Press
會議名稱IEEE International Conference on Information Reuse and Integration (IEEE IRI 2013)
會議地點San Francisco, California, USA
摘要Credit is becoming one of the most important incomes of banking. Past studies indicate that the credit risk scoring model has been better for Logistic Regression and Neural Network. The purpose of this paper is to conduct a comparative study on the accuracy of classification models and reduce the credit risk. In this paper, we use data mining of enterprise software to construct four classification models, namely, decision tree, logistic regression, neural network and support vector machine, for credit scoring in banking. We conduct a systematic comparison and analysis on the accuracy of 17 classification models for credit scoring in banking. The contribution of this paper is that we use different classification methods to construct classification models and compare classification models accuracy, and the evidence demonstrates that the support vector machine models have higher accuracy rates and therefore outperform past classification methods in the context of credit scoring in banking.
關鍵字Classification Method;Credit Risk Score;Data Mining;SAS Enterprise Miner;Support Vector Machine (SVM)
語言英文
收錄於EI
會議性質國際
校內研討會地點
研討會時間20130814~20130816
通訊作者myday@mail.tku.edu.tw (Min-Yuh Day)
國別美國
公開徵稿Y
出版型式電子版
出處Proceedings of the IEEE International Conference on Information Reuse and Integration (IEEE IRI 2013), pp.684-691
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